diff --git a/aiter/configs/a8w8_blockscale_group32_tuned_gemm.csv b/aiter/configs/a8w8_blockscale_group32_tuned_gemm.csv
new file mode 100644
index 00000000000..01f66480f90
--- /dev/null
+++ b/aiter/configs/a8w8_blockscale_group32_tuned_gemm.csv
@@ -0,0 +1 @@
+gfx,cu_num,M,N,K,libtype,kernelId,splitK,us,kernelName,tflops,bw,errRatio
diff --git a/aiter/configs/a8w8_blockscale_group32_untuned_gemm.csv b/aiter/configs/a8w8_blockscale_group32_untuned_gemm.csv
new file mode 100644
index 00000000000..a1b223a5c73
--- /dev/null
+++ b/aiter/configs/a8w8_blockscale_group32_untuned_gemm.csv
@@ -0,0 +1 @@
+M,N,K
diff --git a/aiter/configs/model_configs/dsv41_a8w8_blockscale_group32_tuned_gemm.csv b/aiter/configs/model_configs/dsv41_a8w8_blockscale_group32_tuned_gemm.csv
new file mode 100644
index 00000000000..52bf0adc068
--- /dev/null
+++ b/aiter/configs/model_configs/dsv41_a8w8_blockscale_group32_tuned_gemm.csv
@@ -0,0 +1,1225 @@
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diff --git a/aiter/jit/core.py b/aiter/jit/core.py
index bd023fd0a8a..cb37dbb3381 100644
--- a/aiter/jit/core.py
+++ b/aiter/jit/core.py
@@ -125,6 +125,13 @@ def mp_lock(
     f"{AITER_ROOT_DIR}/aiter/configs/a8w8_blockscale_tuned_gemm.csv",
 )
 
+# Native E8M0 group32 scales have a different operand contract from the
+# FP32 128x128 blockscale family, so shape-identical rows must stay separate.
+AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_GROUP32 = os.getenv(
+    "AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_GROUP32",
+    f"{AITER_ROOT_DIR}/aiter/configs/a8w8_blockscale_group32_tuned_gemm.csv",
+)
+
 AITER_CONFIG_FMOE = os.getenv(
     "AITER_CONFIG_FMOE",
     f"{AITER_ROOT_DIR}/aiter/configs/tuned_fmoe.csv",
@@ -243,6 +250,14 @@ def AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_FILE(self):
             "a8w8_blockscale_tuned_gemm",
         )
 
+    @property
+    def AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_GROUP32_FILE(self):
+        return self.get_config_file(
+            "AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_GROUP32",
+            AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_GROUP32,
+            "a8w8_blockscale_group32_tuned_gemm",
+        )
+
     @property
     def AITER_CONFIG_FMOE_FILE(self):
         return self.get_config_file(
diff --git a/aiter/ops/gemm_op_a8w8.py b/aiter/ops/gemm_op_a8w8.py
index 05b5264d9f7..40c3ad13967 100644
--- a/aiter/ops/gemm_op_a8w8.py
+++ b/aiter/ops/gemm_op_a8w8.py
@@ -803,8 +803,28 @@ def _blockscale_triton(
     x_scale: Tensor,
     w_scale: Tensor,
     dtype: torch.dtype,
+    *,
+    group32: bool = False,
+    split_k: int | None = None,
 ) -> Tensor:
-    """Run the triton kernel on CK's inputs: row-major x_scale, (N, K) weight, JIT per arch."""
+    """Run Triton on unshuffled weights with the selected scale format."""
+    if group32:
+        from aiter.ops.triton.gemm.basic.gemm_a8w8_blockscale_group32 import (
+            gemm_a8w8_blockscale_group32,
+        )
+
+        assert WQ.ndim == w_scale.ndim == 2, "Expected matrix weights and scales"
+        group_n = 32 if w_scale.shape[0] == -(-WQ.shape[0] // 32) else 1
+        return gemm_a8w8_blockscale_group32(
+            XQ,
+            WQ,
+            x_scale,
+            w_scale,
+            dtype=dtype,
+            weight_group_rows=group_n,
+            split_k=split_k,
+        )
+
     from aiter.ops.triton.gemm.basic.gemm_a8w8_blockscale import (
         gemm_a8w8_blockscale as _gemm_a8w8_blockscale_triton,
     )
@@ -821,6 +841,7 @@ def gemm_a8w8_blockscale_fake(
     w_scale: Tensor,
     dtype: torch.dtype = dtypes.bf16,
     isBpreshuffled=False,
+    split_k: int | None = None,
 ) -> torch.Tensor:
     m = XQ.shape[0]
     n = WQ.shape[0]
@@ -828,7 +849,7 @@ def gemm_a8w8_blockscale_fake(
     return Y
 
 
-@torch_compile_guard(gen_fake=gemm_a8w8_blockscale_fake)
+@torch_compile_guard(mutates_args=[], gen_fake=gemm_a8w8_blockscale_fake)
 def gemm_a8w8_blockscale(
     XQ: Tensor,
     WQ: Tensor,
@@ -836,62 +857,107 @@ def gemm_a8w8_blockscale(
     w_scale: Tensor,
     dtype: torch.dtype = dtypes.bf16,
     isBpreshuffled: bool = False,
+    split_k: int | None = None,
 ) -> torch.Tensor:
-    assert dtype in [
-        dtypes.bf16,
-        dtypes.fp16,
-    ], f"Output {dtype=} is currently not supported in gemm_a8w8"
+    """Blockscaled A8W8 GEMM with configuration-first backend dispatch.
+
+    Native E8M0 group32 scales (typed tensors or uint8 views) use
+    AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_GROUP32;
+    FP32 128x128 scales use AITER_CONFIG_GEMM_A8W8_BLOCKSCALE. Both tables are
+    queried through get_CKGEMM_config before choosing a fallback. Group32
+    currently supports libtype="triton", also its default on a config miss.
+    Triton tile and split-K parameters come from its own tuning tables.
+    For native group32 operands, split_k optionally overrides the positive
+    partition count without changing configured backend selection.
+    """
+    is_group32 = (
+        x_scale.dtype in (dtypes.fp8_e8m0, torch.uint8)
+        and w_scale.dtype in (dtypes.fp8_e8m0, torch.uint8)
+        and XQ.ndim == WQ.ndim == 2
+        and x_scale.shape == (XQ.shape[0], XQ.shape[1] // 32)
+        and w_scale.shape
+        in (
+            (WQ.shape[0], WQ.shape[1] // 32),
+            (-(-WQ.shape[0] // 32), WQ.shape[1] // 32),
+        )
+    )
+    # A malformed byte-scale layout must not fall through to FP32 CK dispatch.
+    assert (
+        is_group32 or x_scale.dtype == w_scale.dtype == dtypes.fp32
+    ), "Expected E8M0 group32 scale shapes (typed or uint8), or FP32 128x128 scales"
+    assert (
+        split_k is None or is_group32
+    ), "split_k override requires native group32 operands"
+    assert dtype in (dtypes.bf16, dtypes.fp16) or (
+        is_group32 and dtype == dtypes.fp32
+    ), f"Output {dtype=} is currently not supported in gemm_a8w8"
     m = XQ.shape[0]
     n = WQ.shape[0]
     k = XQ.shape[1]
-    Y = torch.empty(m, n, dtype=dtype, device=XQ.device)
     if isBpreshuffled:
+        assert not is_group32, "Group32 FP8 GEMM requires native weights"
         if get_gfx() in ["gfx950"] and m >= 16 and k >= 512 and dtype == dtypes.bf16:
+            Y = torch.empty(m, n, dtype=dtype, device=XQ.device)
             return gfx950_a8w8_blockscale_ASM(XQ, WQ, x_scale, w_scale, Y)
         else:
             assert 0, "asm kernel only support B preshuffle and m >= 16"
-    else:
-        if not _hip_blockscale_supported():
-            return _blockscale_triton(XQ, WQ, x_scale, w_scale, dtype)
-        config = get_CKGEMM_config(
-            m, n, k, AITER_CONFIGS.AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_FILE
+
+    tuned_file = (
+        AITER_CONFIGS.AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_GROUP32_FILE
+        if is_group32
+        else AITER_CONFIGS.AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_FILE
+    )
+    config = get_CKGEMM_config(m, n, k, tuned_file)
+    if config is not None:
+        libtype = config["libtype"]
+        if libtype == "triton":
+            return _blockscale_triton(
+                XQ, WQ, x_scale, w_scale, dtype, group32=is_group32, split_k=split_k
+            )
+        # CK/CKTile currently consume FP32 128x128 scales. A misconfigured
+        # group32 row must fail instead of reinterpreting its E8M0 bytes.
+        assert not is_group32, f"Unsupported libtype {libtype} for group32 GEMM"
+        splitK = int(config.get("splitK", 0))
+        kernelName = str(config.get("kernelName", ""))
+        Y = torch.empty(m, n, dtype=dtype, device=XQ.device)
+        if libtype == "ck":
+            return gemm_a8w8_blockscale_ck(
+                XQ,
+                WQ,
+                x_scale,
+                w_scale,
+                Y,
+                splitK=splitK,
+                kernelName=kernelName,
+            )
+        elif libtype == "cktile":
+            return gemm_a8w8_blockscale_cktile(
+                XQ,
+                WQ,
+                x_scale,
+                w_scale,
+                Y,
+                splitK=splitK,
+                kernelName=kernelName,
+            )
+        else:
+            assert 0, f"Unsupported libtype {libtype} for gemm_a8w8_blockscale"
+
+    if is_group32 or not _hip_blockscale_supported():
+        return _blockscale_triton(
+            XQ, WQ, x_scale, w_scale, dtype, group32=is_group32, split_k=split_k
         )
-        if config is not None:
-            libtype = config["libtype"]
-            splitK = int(config.get("splitK", 0))
-            kernelName = str(config.get("kernelName", ""))
-            if libtype == "ck":
-                return gemm_a8w8_blockscale_ck(
-                    XQ,
-                    WQ,
-                    x_scale,
-                    w_scale,
-                    Y,
-                    splitK=splitK,
-                    kernelName=kernelName,
-                )
-            elif libtype == "cktile":
-                return gemm_a8w8_blockscale_cktile(
-                    XQ,
-                    WQ,
-                    x_scale,
-                    w_scale,
-                    Y,
-                    splitK=splitK,
-                    kernelName=kernelName,
-                )
-            else:
-                assert 0, f"Unsupported libtype {libtype} for gemm_a8w8_blockscale"
-        min_m = _BLOCKSCALE_TRITON_FALLBACK_MIN_M.get(get_gfx())
-        if min_m is not None and m >= min_m:
-            return _blockscale_triton(XQ, WQ, x_scale, w_scale, dtype)
-        try:
-            return gemm_a8w8_blockscale_ck(XQ, WQ, x_scale, w_scale, Y)
-        except RuntimeError as e:
-            raise RuntimeError(
-                f"gemm_a8w8_blockscale failed for shape M={m}, N={n}, K={k}, "
-                f"{dtype=}, config={config}: {e}"
-            ) from e
+    min_m = _BLOCKSCALE_TRITON_FALLBACK_MIN_M.get(get_gfx())
+    if min_m is not None and m >= min_m:
+        return _blockscale_triton(XQ, WQ, x_scale, w_scale, dtype)
+    Y = torch.empty(m, n, dtype=dtype, device=XQ.device)
+    try:
+        return gemm_a8w8_blockscale_ck(XQ, WQ, x_scale, w_scale, Y)
+    except RuntimeError as e:
+        raise RuntimeError(
+            f"gemm_a8w8_blockscale failed for shape M={m}, N={n}, K={k}, "
+            f"{dtype=}, config={config}: {e}"
+        ) from e
 
 
 def flatmm_a8w8_blockscale_ASM(
diff --git a/aiter/ops/triton/_triton_kernels/gemm/basic/gemm_a8w8_blockscale_group32.py b/aiter/ops/triton/_triton_kernels/gemm/basic/gemm_a8w8_blockscale_group32.py
new file mode 100644
index 00000000000..ecff2cbc0cb
--- /dev/null
+++ b/aiter/ops/triton/_triton_kernels/gemm/basic/gemm_a8w8_blockscale_group32.py
@@ -0,0 +1,295 @@
+# SPDX-License-Identifier: MIT
+# Copyright (C) 2026, Advanced Micro Devices, Inc. All rights reserved.
+"""Native E4M3/E8M0 group32 GEMMs, including packed-K small-M reductions."""
+
+import triton
+import triton.language as tl
+
+from aiter.ops.triton.utils._triton.kernel_repr import make_kernel_repr
+from aiter.ops.triton.utils.gemm_config_utils import get_gemm_config
+
+_gemm_group32_repr = make_kernel_repr(
+    "_gemm_a8w8_blockscale_group32_kernel",
+    [
+        "BLOCK_SIZE_M",
+        "BLOCK_SIZE_N",
+        "BLOCK_SIZE_K",
+        "GROUP_N",
+        "SPLITK_BLOCK_SIZE",
+        "FUSED_SPLITS",
+        "N_FIRST",
+        "B_CACHE_MODIFIER",
+        "N",
+        "K",
+        "LAUNCH_OPTIONS",
+    ],
+)
+_gemm_group32_packed_repr = make_kernel_repr(
+    "_gemm_a8w8_blockscale_group32_packed_kernel",
+    [
+        "BLOCK_SIZE_M",
+        "BLOCK_SIZE_N",
+        "BLOCK_SIZE_K",
+        "K_PACK",
+        "SPLITK_BLOCK_SIZE",
+        "FUSED_SPLITS",
+        "B_CACHE_MODIFIER",
+        "N",
+        "K",
+        "LAUNCH_OPTIONS",
+    ],
+)
+
+
+@triton.jit
+def _tile_on_xcd(N: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, SPLITS: tl.constexpr):
+    """(pid_m, pid_n, tile, split) of a 1D grid, all splits of a tile on one XCD.
+
+    CTAs go to XCDs by pid % 8, a multiple of every gfx950 XCD count.
+    """
+    pid = tl.program_id(0)
+    tile = (pid // 8 // SPLITS) * 8 + pid % 8
+    grid_n: tl.constexpr = (N + BLOCK_SIZE_N - 1) // BLOCK_SIZE_N
+    return tile // grid_n, tile % grid_n, tile, pid // 8 % SPLITS
+
+
+@triton.jit
+def _sum_splits_on_xcd(
+    partial, c_ptrs, mask, slot, counter, split, SPLITS: tl.constexpr
+):
+    """The tile's last CTA to arrive sums its splits in split order.
+
+    The splits share one XCD's L2, so a completed store is visible without an
+    agent-scope release and its L2 writeback.
+    """
+    BM: tl.constexpr = partial.shape[0]
+    BN: tl.constexpr = partial.shape[1]
+    local = tl.arange(0, BM)[:, None] * BN + tl.arange(0, BN)[None, :]
+    tl.store(slot + split * BM * BN + local, partial)
+    # debug_barrier is a bare s_barrier: drain this wave's stores first.
+    tl.inline_asm_elementwise(
+        "s_waitcnt vmcnt(0)", "=v,v", [split], dtype=tl.int32, is_pure=False, pack=1
+    )
+    tl.debug_barrier()
+    if tl.atomic_add(counter, 1, sem="acq_rel", scope="cta") == SPLITS - 1:
+        total = tl.zeros((BM, BN), tl.float32)
+        for s in tl.static_range(SPLITS):
+            # .cv skips this CU's L1, which never saw the other splits.
+            total += tl.load(slot + s * BM * BN + local, cache_modifier=".cv")
+        tl.store(c_ptrs, total.to(c_ptrs.dtype.element_ty), mask)
+        tl.store(counter, 0)
+
+
+@triton.jit(repr=_gemm_group32_repr, do_not_specialize=["M"])
+def _gemm_a8w8_blockscale_group32_kernel(
+    a_ptr,
+    b_ptr,
+    a_scale_ptr,
+    b_scale_ptr,
+    c_ptr,
+    ws_ptr,
+    cnt_ptr,
+    # Runtime, not constexpr: M is the token count, and specializing on it
+    # recompiles the kernel for every prefill chunk length.
+    M,
+    N: tl.constexpr,
+    K: tl.constexpr,
+    GROUP_N: tl.constexpr,
+    SPLITK_BLOCK_SIZE: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr,
+    BLOCK_SIZE_N: tl.constexpr,
+    BLOCK_SIZE_K: tl.constexpr,
+    LAUNCH_OPTIONS: tl.constexpr,
+    N_FIRST: tl.constexpr = False,
+    FUSED_SPLITS: tl.constexpr = 1,
+    B_CACHE_MODIFIER: tl.constexpr = None,
+):
+    """E4M3 x E4M3 with E8M0 group scales, on the CDNA4 microscaling MFMA.
+
+    A tile spans BLOCK_SIZE_K/32 scale groups. Below a K tile of 64,
+    dot_scaled lowers to slower BF16 emulation instead of microscaling MFMA.
+    FUSED_SPLITS > 1 sums split-K partials in this launch on a 1D grid;
+    otherwise c_ptr takes one FP32 partial per program_id(2).
+    B_CACHE_MODIFIER applies to the weight and weight-scale loads.
+    """
+    tl.static_assert(BLOCK_SIZE_K >= 64 and BLOCK_SIZE_K % 32 == 0)
+    if FUSED_SPLITS > 1:
+        pid_m, pid_n, tile, split = _tile_on_xcd(N, BLOCK_SIZE_N, FUSED_SPLITS)
+        if pid_m * BLOCK_SIZE_M >= M:
+            return
+    else:
+        # The first grid dimension advances fastest. N-first traversal reuses
+        # A; M-first traversal reuses B. The wrapper swaps the launch dimensions.
+        pid_m = tl.program_id(1 if N_FIRST else 0)
+        pid_n = tl.program_id(0 if N_FIRST else 1)
+        split = tl.program_id(2)
+    row = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    col = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    groups: tl.constexpr = K // 32
+    ks = split * SPLITK_BLOCK_SIZE + tl.arange(0, BLOCK_SIZE_K)
+    gs = split * SPLITK_BLOCK_SIZE // 32 + tl.arange(0, BLOCK_SIZE_K // 32)
+    rows = row[:, None] < M
+    cols = col[:, None] < N
+    a_ptrs = a_ptr + row[:, None] * K + ks[None, :]
+    b_ptrs = b_ptr + col[:, None] * K + ks[None, :]
+    as_ptrs = a_scale_ptr + row[:, None] * groups + gs[None, :]
+    # One scale row per GROUP_N output columns, so the column index is
+    # divided rather than the grid expanded.
+    bs_ptrs = b_scale_ptr + (col[:, None] // GROUP_N) * groups + gs[None, :]
+
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), tl.float32)
+    # A constexpr trip count lets the pipeliner schedule statically. Only a
+    # last split with fewer K tiles than the others stops early, at K.
+    LAST: tl.constexpr = K - (K - 1) // SPLITK_BLOCK_SIZE * SPLITK_BLOCK_SIZE
+    if (LAST + BLOCK_SIZE_K - 1) // BLOCK_SIZE_K == SPLITK_BLOCK_SIZE // BLOCK_SIZE_K:
+        span = SPLITK_BLOCK_SIZE
+    else:
+        span = tl.minimum(SPLITK_BLOCK_SIZE, K - split * SPLITK_BLOCK_SIZE)
+    for i in range(0, span, BLOCK_SIZE_K):
+        # K masks only when K has a partial tile.
+        a_mask, b_mask, as_mask, bs_mask = rows, cols, rows, cols
+        if K % BLOCK_SIZE_K != 0:
+            live = (ks + i < K)[None, :]
+            held = (gs + i // 32 < groups)[None, :]
+            a_mask, b_mask = rows & live, cols & live
+            as_mask, bs_mask = rows & held, cols & held
+        a = tl.load(a_ptrs, a_mask, other=0.0)
+        b = tl.load(b_ptrs, b_mask, other=0.0, cache_modifier=B_CACHE_MODIFIER)
+        a_code = tl.load(as_ptrs, as_mask, other=127)
+        b_code = tl.load(bs_ptrs, bs_mask, other=127, cache_modifier=B_CACHE_MODIFIER)
+        # acc= leaves the sum in the matrix core's registers: one rounding per
+        # tile instead of two, and no separate vector add.
+        accumulator = tl.dot_scaled(
+            a, a_code, "e4m3", b.T, b_code, "e4m3", acc=accumulator
+        )
+        a_ptrs += BLOCK_SIZE_K
+        b_ptrs += BLOCK_SIZE_K
+        as_ptrs += BLOCK_SIZE_K // 32
+        bs_ptrs += BLOCK_SIZE_K // 32
+    c_ptrs = c_ptr + row[:, None] * N + col[None, :]
+    if FUSED_SPLITS > 1:
+        _sum_splits_on_xcd(
+            accumulator,
+            c_ptrs,
+            rows & cols.T,
+            ws_ptr + tile * (FUSED_SPLITS * BLOCK_SIZE_M * BLOCK_SIZE_N),
+            cnt_ptr + tile,
+            split,
+            FUSED_SPLITS,
+        )
+    else:
+        tl.store(c_ptrs + split * M * N, accumulator, rows & cols.T)
+
+
+@triton.jit(repr=_gemm_group32_packed_repr, do_not_specialize=["M"])
+def _gemm_a8w8_blockscale_group32_packed_kernel(
+    a_ptr,
+    b_ptr,
+    a_scale_ptr,
+    b_scale_ptr,
+    c_ptr,
+    ws_ptr,
+    cnt_ptr,
+    M,
+    N: tl.constexpr,
+    K: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr,
+    BLOCK_SIZE_N: tl.constexpr,
+    BLOCK_SIZE_K: tl.constexpr,
+    K_PACK: tl.constexpr,
+    LAUNCH_OPTIONS: tl.constexpr,
+    SPLITK_BLOCK_SIZE: tl.constexpr = 0,
+    FUSED_SPLITS: tl.constexpr = 1,
+    B_CACHE_MODIFIER: tl.constexpr = None,
+):
+    """Small-M group32 GEMM with K panels packed into MFMA rows/columns.
+
+    Each packed row/column retains its own E8M0 scales. Only matching panel
+    pairs contribute to the output; cross-panel products are discarded. One
+    CTA owns the K reduction, or with FUSED_SPLITS > 1 one SPLITK_BLOCK_SIZE
+    of it, summed in this launch. BLOCK_SIZE_M is an unpacked token tile,
+    independent of runtime M. B_CACHE_MODIFIER applies to the weight and
+    weight-scale loads.
+    """
+    tl.static_assert(K_PACK == 1 or K_PACK == 2 or K_PACK == 4)
+    tl.static_assert(BLOCK_SIZE_M * K_PACK >= 16)
+    tl.static_assert(BLOCK_SIZE_K >= 128 and BLOCK_SIZE_K % 32 == 0)
+    STEP: tl.constexpr = BLOCK_SIZE_K * K_PACK
+    if FUSED_SPLITS > 1:
+        tl.static_assert(SPLITK_BLOCK_SIZE % STEP == 0)
+        pid_m, pid_n, tile, split = _tile_on_xcd(N, BLOCK_SIZE_N, FUSED_SPLITS)
+        if pid_m * BLOCK_SIZE_M >= M:
+            return
+        start = split * SPLITK_BLOCK_SIZE
+        stop = tl.minimum(start + SPLITK_BLOCK_SIZE, K)
+    else:
+        pid_m = tl.program_id(0)
+        pid_n = tl.program_id(1)
+        start = 0
+        stop = K
+    rows = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M * K_PACK) // K_PACK
+    a_panel = tl.arange(0, BLOCK_SIZE_M * K_PACK) % K_PACK
+    cols = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N * K_PACK) // K_PACK
+    b_panel = tl.arange(0, BLOCK_SIZE_N * K_PACK) % K_PACK
+    ks = tl.arange(0, BLOCK_SIZE_K)
+    gs = tl.arange(0, BLOCK_SIZE_K // 32)
+    groups: tl.constexpr = K // 32
+    accumulator = tl.zeros((BLOCK_SIZE_M * K_PACK, BLOCK_SIZE_N * K_PACK), tl.float32)
+    for base in range(start, stop, STEP):
+        ak = base + a_panel[:, None] * BLOCK_SIZE_K + ks[None, :]
+        bk = base + b_panel[:, None] * BLOCK_SIZE_K + ks[None, :]
+        a = tl.load(
+            a_ptr + rows[:, None] * K + ak,
+            (rows[:, None] < M) & (ak < K),
+            other=0.0,
+        )
+        b = tl.load(
+            b_ptr + cols[:, None] * K + bk,
+            (cols[:, None] < N) & (bk < K),
+            other=0.0,
+            cache_modifier=B_CACHE_MODIFIER,
+        )
+        ag = base // 32 + a_panel[:, None] * (BLOCK_SIZE_K // 32) + gs[None, :]
+        bg = base // 32 + b_panel[:, None] * (BLOCK_SIZE_K // 32) + gs[None, :]
+        a_code = tl.load(
+            a_scale_ptr + rows[:, None] * groups + ag,
+            (rows[:, None] < M) & (ag < groups),
+            other=127,
+        )
+        b_code = tl.load(
+            b_scale_ptr + (cols[:, None] // 32) * groups + bg,
+            (cols[:, None] < N) & (bg < groups),
+            other=127,
+            cache_modifier=B_CACHE_MODIFIER,
+        )
+        accumulator = tl.dot_scaled(
+            a, a_code, "e4m3", b.T, b_code, "e4m3", acc=accumulator
+        )
+    panels = accumulator.reshape(BLOCK_SIZE_M, K_PACK, BLOCK_SIZE_N, K_PACK).trans(
+        0, 2, 1, 3
+    )
+    pair = tl.arange(0, K_PACK)
+    diagonal = tl.where(
+        pair[None, None, :, None] == pair[None, None, None, :], panels, 0.0
+    )
+    output = tl.sum(tl.sum(diagonal, 3), 2)
+    row = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    col = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    c_ptrs = c_ptr + row[:, None] * N + col[None, :]
+    mask = (row[:, None] < M) & (col[None, :] < N)
+    if FUSED_SPLITS > 1:
+        _sum_splits_on_xcd(
+            output,
+            c_ptrs,
+            mask,
+            ws_ptr + tile * (FUSED_SPLITS * BLOCK_SIZE_M * BLOCK_SIZE_N),
+            cnt_ptr + tile,
+            split,
+            FUSED_SPLITS,
+        )
+    else:
+        tl.store(c_ptrs, output, mask)
+
+
+def _get_config(M: int, N: int, K: int):
+    return get_gemm_config("GEMM-A8W8_BLOCKSCALE_GROUP32", M, N, K)
diff --git a/aiter/ops/triton/configs/CLAUDE.md b/aiter/ops/triton/configs/CLAUDE.md
index 985e8185238..119aa5813c1 100644
--- a/aiter/ops/triton/configs/CLAUDE.md
+++ b/aiter/ops/triton/configs/CLAUDE.md
@@ -191,7 +191,9 @@ The returned config is a fresh deep copy, safe to mutate.
 - `M_LEQ_x` is searched over `STANDARD_M_BOUNDS =
   (1, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192)`. A caller may
   override with `bounds=(...)`, which must be strictly increasing positive
-  ints.
+  ints. A file may instead declare a top-level `M_BOUNDS` list to keep
+  nonstandard tuning boundaries in JSON. Explicit `bounds=` wins; files
+  without `M_BOUNDS` retain the standard bounds.
 - `any` must exist unless every reachable `M` is covered by an explicit bound.
   A `KeyError` at lookup time usually means it is missing.
 - The deprecated `{"large": …, "small": …}` shape must not be introduced.
diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/DEFAULT.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/DEFAULT.json
new file mode 100644
index 00000000000..cb5a173e362
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/DEFAULT.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=1152-K=5120.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=1152-K=5120.json
new file mode 100644
index 00000000000..7fa212b7fea
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=1152-K=5120.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=1536-K=5120.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=1536-K=5120.json
new file mode 100644
index 00000000000..4d6b29064fd
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=1536-K=5120.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=16384-K=1280.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=16384-K=1280.json
new file mode 100644
index 00000000000..a6432a621d8
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=16384-K=1280.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=1792-K=5120.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=1792-K=5120.json
new file mode 100644
index 00000000000..e5c77e684f8
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=1792-K=5120.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=2304-K=5120.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=2304-K=5120.json
new file mode 100644
index 00000000000..9d7f68c75ef
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=2304-K=5120.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=25600-K=6144.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=25600-K=6144.json
new file mode 100644
index 00000000000..43c1029423c
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=25600-K=6144.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=32768-K=1280.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=32768-K=1280.json
new file mode 100644
index 00000000000..06be301e94e
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=32768-K=1280.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=4096-K=1280.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=4096-K=1280.json
new file mode 100644
index 00000000000..2f1e7293c8d
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=4096-K=1280.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=4608-K=5120.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=4608-K=5120.json
new file mode 100644
index 00000000000..ad927a532d3
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=4608-K=5120.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=512-K=5120.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=512-K=5120.json
new file mode 100644
index 00000000000..484be8ce21d
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=512-K=5120.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=1152.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=1152.json
new file mode 100644
index 00000000000..4120b736633
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=1152.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=15360.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=15360.json
new file mode 100644
index 00000000000..ffcd2c6bb6b
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=15360.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=2048.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=2048.json
new file mode 100644
index 00000000000..048b23d1e12
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=2048.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=2304.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=2304.json
new file mode 100644
index 00000000000..e2718e3e3cf
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=2304.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=4096.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=4096.json
new file mode 100644
index 00000000000..beb4290ff1e
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=4096.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=576.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=576.json
new file mode 100644
index 00000000000..26094c9bbfe
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=576.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=8192.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=8192.json
new file mode 100644
index 00000000000..a7e0d854386
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=5120-K=8192.json
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diff --git a/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=8192-K=1280.json b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=8192-K=1280.json
new file mode 100644
index 00000000000..57620212ffd
--- /dev/null
+++ b/aiter/ops/triton/configs/gfx950/triton/gemm/gemm_a8w8_blockscale_group32/GEMM-A8W8_BLOCKSCALE_GROUP32-N=8192-K=1280.json
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+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": ".cg",
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_864": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32,
+    "FUSED_SPLITK": false
+  },
+  "M_LEQ_896": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 4,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": ".cg",
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_928": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 4,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_960": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 4,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": ".cg",
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_992": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 4,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": ".cg",
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_1024": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 4,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_1088": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 4,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_1152": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 4,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_1216": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32,
+    "FUSED_SPLITK": false
+  },
+  "M_LEQ_1280": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32,
+    "FUSED_SPLITK": false
+  },
+  "M_LEQ_1344": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32,
+    "FUSED_SPLITK": false
+  },
+  "M_LEQ_1408": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32,
+    "FUSED_SPLITK": false
+  },
+  "M_LEQ_1472": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32,
+    "FUSED_SPLITK": false
+  },
+  "M_LEQ_1536": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32,
+    "FUSED_SPLITK": false
+  },
+  "M_LEQ_1600": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_1664": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_1728": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32,
+    "FUSED_SPLITK": false
+  },
+  "M_LEQ_1792": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32,
+    "FUSED_SPLITK": false
+  },
+  "M_LEQ_1856": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_1920": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_1984": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_2048": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_4096": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_6144": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "num_warps": 4,
+    "num_stages": 1,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 0,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": false,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_8192": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32,
+    "FUSED_SPLITK": false
+  },
+  "M_LEQ_10240": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "num_warps": 4,
+    "num_stages": 1,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 0,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": false,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_12288": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "num_warps": 4,
+    "num_stages": 1,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 0,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": false,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_14336": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "num_warps": 4,
+    "num_stages": 1,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 0,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": false,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_16384": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 4,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 0,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "M_LEQ_32768": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "num_warps": 8,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 32,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": true,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  },
+  "any": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "num_warps": 4,
+    "num_stages": 1,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 0,
+    "cache_modifier": null,
+    "NUM_KSPLIT": 1,
+    "N_FIRST": false,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32
+  }
+}
diff --git a/aiter/ops/triton/gemm/basic/gemm_a8w8_blockscale_group32.py b/aiter/ops/triton/gemm/basic/gemm_a8w8_blockscale_group32.py
new file mode 100644
index 00000000000..89049b42169
--- /dev/null
+++ b/aiter/ops/triton/gemm/basic/gemm_a8w8_blockscale_group32.py
@@ -0,0 +1,253 @@
+# SPDX-License-Identifier: MIT
+# Copyright (C) 2026, Advanced Micro Devices, Inc. All rights reserved.
+
+import torch
+import triton
+
+from aiter.jit.utils.chip_info import get_gfx_runtime
+from aiter.ops.triton._triton_kernels.common.splitk_reduce import (
+    _gemm_splitk_reduce_kernel,
+)
+from aiter.ops.triton._triton_kernels.gemm.basic.gemm_a8w8_blockscale_group32 import (
+    _gemm_a8w8_blockscale_group32_kernel,
+    _gemm_a8w8_blockscale_group32_packed_kernel,
+    _get_config,
+)
+from aiter.utility.graph_alloc import ROUTES_INSIDE_CAPTURE, persistent_alloc
+
+# One arrival counter per output tile of a fused split-K launch.
+_SPLIT_COUNTERS = 1 << 16
+_split_counters_by_stream: dict[tuple[torch.device, int], torch.Tensor] = {}
+
+
+def _split_counters(device: torch.device) -> torch.Tensor | None:
+    """Zeroed arrival counters per (device, stream), as for the a16w16 split-K
+    semaphores; kernels re-zero what they use. None when a stream's first use
+    falls inside a capture that cannot allocate outside the graph pool."""
+    key = (device, torch.cuda.current_stream(device).cuda_stream)
+    counters = _split_counters_by_stream.get(key)
+    if counters is None:
+        if not ROUTES_INSIDE_CAPTURE and torch.cuda.is_current_stream_capturing():
+            return None
+        with persistent_alloc(device):
+            counters = torch.zeros(_SPLIT_COUNTERS, dtype=torch.int32, device=device)
+        _split_counters_by_stream[key] = counters
+    return counters
+
+
+def _split_k_partition(k: int, splits: int, step: int) -> tuple[int, int]:
+    """Partition size rounded up to whole steps, and the non-empty count."""
+    part = -(-k // splits)
+    size = -(-part // step) * step
+    return size, -(-k // size)
+
+
+def _fused_workspace(y, tiles, splits, block_m, block_n):
+    """(grid, partials, counters) for fused split-K, or None to run unfused."""
+    assert tiles <= _SPLIT_COUNTERS, "Too many output tiles for fused split-K"
+    counters = _split_counters(y.device)
+    if counters is None:
+        return None
+    partials = torch.empty(
+        tiles * splits * block_m * block_n, dtype=torch.float32, device=y.device
+    )
+    return (-(-tiles // 8) * 8 * splits,), partials, counters
+
+
+def gemm_a8w8_blockscale_group32(
+    x: torch.Tensor,
+    w: torch.Tensor,
+    x_scale: torch.Tensor,
+    w_scale: torch.Tensor,
+    dtype: torch.dtype = torch.bfloat16,
+    y: torch.Tensor | None = None,
+    weight_group_rows: int = 32,
+    split_k: int | None = None,
+    config: dict | None = None,
+) -> torch.Tensor:
+    """Compute X @ W.T using native E4M3 operands and E8M0 group32 scales.
+
+    x and w are contiguous (M, K) and (N, K) matrices. Activation scales are
+    row-major (M, K // 32); compact weight scales are
+    (ceil(N / weight_group_rows), K // 32), with weight_group_rows 1 or 32.
+    E8M0 and E4M3 tensors may also be passed as their uint8 views. Weights and
+    scales are neither preshuffled nor expanded.
+
+    Accumulation is FP32, with BF16, FP16 or FP32 output. y optionally supplies
+    a contiguous (M, N) output buffer. split_k is a positive partition count;
+    None uses NUM_KSPLIT and the optional packed-K variant from the tuning table.
+    config optionally overrides the table, following the Triton GEMM interface.
+    Explicit partitions are rounded to whole K tiles, then empty partitions
+    are removed. Split-K uses the shared AITER reduction without atomics;
+    a table FUSED_SPLITK (or packed NUM_KSPLIT) instead sums each tile's
+    partitions within the launch, on one XCD. A table cache_modifier applies
+    to the weight loads.
+
+    This backend requires gfx950 microscaling MFMA. Small M is limited by
+    weight bandwidth and occupancy; packing K panels fills MFMA rows without
+    a global partial buffer. Larger M uses output tiling and activation reuse.
+    """
+    assert x.ndim == w.ndim == 2, "Expected two matrix operands"
+    M, K = x.shape
+    N, weight_k = w.shape
+    assert (
+        K > 0 and K % 32 == 0 and weight_k == K and N > 0
+    ), "Expected matching positive K divisible by 32 and positive N"
+    assert weight_group_rows in (1, 32), "Weight scales must be 1x32 or 32x32"
+    assert x.dtype in (torch.float8_e4m3fn, torch.uint8), "x must be E4M3"
+    assert w.dtype in (torch.float8_e4m3fn, torch.uint8), "w must be E4M3"
+    assert x_scale.dtype in (torch.float8_e8m0fnu, torch.uint8), "x_scale must be E8M0"
+    assert w_scale.dtype in (torch.float8_e8m0fnu, torch.uint8), "w_scale must be E8M0"
+    assert x_scale.shape == (M, K // 32), "Invalid activation scale shape"
+    assert w_scale.shape == (
+        -(-N // weight_group_rows),
+        K // 32,
+    ), "Invalid weight scale shape"
+    assert all(
+        t.is_cuda and t.device == x.device and t.is_contiguous()
+        for t in (x, w, x_scale, w_scale)
+    ), "Operands must be contiguous on the same GPU"
+    assert dtype in (
+        torch.bfloat16,
+        torch.float16,
+        torch.float32,
+    ), "Output must be BF16, FP16 or FP32"
+    assert split_k is None or (
+        type(split_k) is int and split_k > 0
+    ), "split_k must be a positive integer or None"
+    if y is None:
+        y = torch.empty((M, N), dtype=dtype, device=x.device)
+    else:
+        assert (
+            y.shape == (M, N) and y.dtype == dtype and y.device == x.device
+        ), "Invalid output shape, dtype or device"
+        assert y.is_contiguous(), "Output must be contiguous"
+    if M == 0:
+        return y
+    assert get_gfx_runtime() == "gfx950", "Group32 FP8 GEMM requires gfx950"
+
+    x = x.view(torch.float8_e4m3fn) if x.dtype == torch.uint8 else x
+    w = w.view(torch.float8_e4m3fn) if w.dtype == torch.uint8 else w
+    x_scale = x_scale.view(torch.uint8)
+    w_scale = w_scale.view(torch.uint8)
+    if config is None:
+        config, _ = _get_config(M, N, K)
+    packed = (
+        config.get("packed") if weight_group_rows == 32 and split_k is None else None
+    )
+    launch_config = config if packed is None else packed
+    launch_options = {
+        key: launch_config[key]
+        for key in ("num_warps", "num_stages", "waves_per_eu", "matrix_instr_nonkdim")
+    }
+    # Triton's repr callback sees constexpr arguments, not compiler options.
+    # Carry the same values into the name without a second source of tuning.
+    launch_repr = tuple(launch_options.items())
+    if packed is not None:
+        block_m, block_n = packed["BLOCK_SIZE_M"], packed["BLOCK_SIZE_N"]
+        block_k, k_pack = packed["BLOCK_SIZE_K"], packed["K_PACK"]
+        grid_m, grid_n = -(-M // block_m), -(-N // block_n)
+        split_k_size, num_splits = _split_k_partition(
+            K, packed.get("NUM_KSPLIT", 1), block_k * k_pack
+        )
+        workspace = None
+        if num_splits > 1:
+            workspace = _fused_workspace(
+                y, grid_m * grid_n, num_splits, block_m, block_n
+            )
+        if workspace is None:
+            workspace = (grid_m, grid_n), y, y
+            split_k_size, num_splits = 0, 1
+        grid, partials, counters = workspace
+        _gemm_a8w8_blockscale_group32_packed_kernel[grid](
+            x,
+            w,
+            x_scale,
+            w_scale,
+            y,
+            partials,
+            counters,
+            M,
+            N,
+            K,
+            block_m,
+            block_n,
+            block_k,
+            k_pack,
+            LAUNCH_OPTIONS=launch_repr,
+            SPLITK_BLOCK_SIZE=split_k_size,
+            FUSED_SPLITS=num_splits,
+            B_CACHE_MODIFIER=packed.get("cache_modifier"),
+            **launch_options,
+        )
+        return y
+
+    block_m = config["BLOCK_SIZE_M"]
+    block_n = config["BLOCK_SIZE_N"]
+    block_k = config["BLOCK_SIZE_K"]
+    n_first = config["N_FIRST"]
+    grid_m, grid_n = -(-M // block_m), -(-N // block_n)
+    fused = split_k is None and config.get("FUSED_SPLITK", False)
+    split_k = config["NUM_KSPLIT"] if split_k is None else split_k
+    split_k_size, num_splits = _split_k_partition(K, split_k, block_k)
+    workspace = None
+    if fused and num_splits > 1:
+        workspace = _fused_workspace(y, grid_m * grid_n, num_splits, block_m, block_n)
+    if workspace is not None:
+        grid, partials, counters = workspace
+        out, fused_splits = y, num_splits
+    else:
+        out = (
+            y
+            if num_splits == 1
+            else torch.empty((num_splits, M, N), dtype=torch.float32, device=x.device)
+        )
+        grid = (grid_n, grid_m, num_splits) if n_first else (grid_m, grid_n, num_splits)
+        partials = counters = out
+        fused_splits = 1
+    _gemm_a8w8_blockscale_group32_kernel[grid](
+        x,
+        w,
+        x_scale,
+        w_scale,
+        out,
+        partials,
+        counters,
+        M,
+        N,
+        K,
+        weight_group_rows,
+        split_k_size,
+        block_m,
+        block_n,
+        block_k,
+        N_FIRST=n_first,
+        LAUNCH_OPTIONS=launch_repr,
+        FUSED_SPLITS=fused_splits,
+        B_CACHE_MODIFIER=config.get("cache_modifier"),
+        **launch_options,
+    )
+    if fused_splits == 1 and num_splits > 1:
+        reduce_m = config["REDUCE_BLOCK_SIZE_M"]
+        reduce_n = config["REDUCE_BLOCK_SIZE_N"]
+        _gemm_splitk_reduce_kernel[(-(-M // reduce_m), -(-N // reduce_n))](
+            out,
+            y,
+            None,
+            M,
+            N,
+            M * N,
+            N,
+            1,
+            N,
+            1,
+            BLOCK_SIZE_M=reduce_m,
+            BLOCK_SIZE_N=reduce_n,
+            ACTUAL_KSPLIT=num_splits,
+            MAX_KSPLIT=triton.next_power_of_2(num_splits),
+            ADD_BIAS=False,
+            activation=None,
+            use_activation=False,
+            KERNEL_NAME="_gemm_a8w8_blockscale_group32_reduce_kernel",
+        )
+    return y
diff --git a/aiter/ops/triton/utils/gemm_config_utils.py b/aiter/ops/triton/utils/gemm_config_utils.py
index 14d75d14af9..17c00f1ed83 100644
--- a/aiter/ops/triton/utils/gemm_config_utils.py
+++ b/aiter/ops/triton/utils/gemm_config_utils.py
@@ -86,8 +86,15 @@ def _get_gemm_config_cached(
             config_dict, is_tuned = specialized_config, True
             break
 
-    # use standard bounds unless custom bounds are passed
-    search_bounds = bounds if bounds is not None else STANDARD_M_BOUNDS
+    # Explicit bounds override file-defined bounds; legacy files use the standard bounds.
+    search_bounds = (
+        bounds if bounds is not None else config_dict.get("M_BOUNDS", STANDARD_M_BOUNDS)
+    )
+    assert (
+        len(search_bounds) > 0
+        and all(type(x) is int and x > 0 for x in search_bounds)
+        and all(x < y for x, y in itertools.pairwise(search_bounds))
+    ), "M_BOUNDS must be a non-empty sequence of strictly increasing positive integers"
 
     # Search for M_LEQ_x keys
     for bound in search_bounds:
@@ -110,6 +117,18 @@ def _get_gemm_config_cached(
     )
 
 
+def _copy_gemm_config(config: dict) -> dict:
+    # JSON leaves are immutable. Avoid deepcopy's per-scalar overhead on the
+    # GEMM launch path while keeping nested variant configs safe to mutate.
+    result = config.copy()
+    for key, value in config.items():
+        if isinstance(value, dict):
+            result[key] = _copy_gemm_config(value)
+        elif isinstance(value, list):
+            result[key] = copy.deepcopy(value)
+    return result
+
+
 def get_gemm_config(
     config_name: str,
     M: int,
@@ -129,7 +148,7 @@ def get_gemm_config(
     2. If B, N and K are provided, try B-specialized config: {config_name}-B={B}-N={N}-K={K}.json
     3. If N and K are provided, try to load specialized config: {config_name}-N={N}-K={K}.json
        Or if specialized_filename is provided, use: {config_name}-{specialized_filename}.json
-    4. Search for M_LEQ_x keys in order of bounds (default: STANDARD_M_BOUNDS)
+    4. Search M_LEQ_x using explicit bounds, file M_BOUNDS or STANDARD_M_BOUNDS
     5. If no M_LEQ_x matches, search for M_GEQ_x keys in reverse order
     6. Fall back to "any" if no bounds match
 
@@ -138,7 +157,7 @@ def get_gemm_config(
         M: M dimension of the GEMM
         N: N dimension of the GEMM (optional)
         K: K dimension of the GEMM (optional)
-        bounds: Custom bounds to use instead of STANDARD_M_BOUNDS (optional)
+        bounds: Override the file M_BOUNDS or STANDARD_M_BOUNDS (optional)
         specialized_filename: Custom specialized filename suffix (optional)
         backend: Backend whose config directory to read, "triton" (default)
             or "gluon". Declared by the caller; there is no fallback to
@@ -152,7 +171,7 @@ def get_gemm_config(
     config, is_tuned = _get_gemm_config_cached(
         config_name, M, N, K, bounds, specialized_filename, backend, B
     )
-    return copy.deepcopy(config), is_tuned
+    return _copy_gemm_config(config), is_tuned
 
 
 def add_default_gemm_config_params(config: dict) -> dict:
diff --git a/op_tests/op_benchmarks/triton/bench_gemm_a8w8_blockscale_group32.py b/op_tests/op_benchmarks/triton/bench_gemm_a8w8_blockscale_group32.py
new file mode 100644
index 00000000000..cbecc2baf1a
--- /dev/null
+++ b/op_tests/op_benchmarks/triton/bench_gemm_a8w8_blockscale_group32.py
@@ -0,0 +1,131 @@
+# SPDX-License-Identifier: MIT
+# Copyright (C) 2026, Advanced Micro Devices, Inc. All rights reserved.
+"""Benchmark native group32 FP8 GEMM through its production config dispatch."""
+
+import functools
+import math
+
+import torch
+import triton
+
+from aiter.ops.triton.gemm.basic.gemm_a8w8_blockscale_group32 import (
+    gemm_a8w8_blockscale_group32,
+)
+from op_tests.op_benchmarks.triton.utils.argparse import (
+    add_argparse_ff,
+    get_ff_args,
+    get_parser,
+)
+from op_tests.op_benchmarks.triton.utils.benchmark_utils import (
+    get_caller_name_no_ext,
+    get_model_benchmark_object,
+    get_shape_benchmark_object,
+    print_vgpr,
+)
+from op_tests.triton_tests.gemm.basic.test_gemm_a8w8_blockscale_group32 import (
+    generate_inputs,
+    run_torch,
+)
+
+DTYPES = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}
+
+
+def bench_gemm_fn(m, n, k, metric, args):
+    dtype = DTYPES[args.dtype]
+    x, w, xs, ws = generate_inputs(m, n, k, args.weight_group_rows)
+    y = torch.empty((m, n), dtype=dtype, device=x.device)
+    run = functools.partial(
+        gemm_a8w8_blockscale_group32,
+        x,
+        w,
+        xs,
+        ws,
+        dtype=dtype,
+        y=y,
+        weight_group_rows=args.weight_group_rows,
+    )
+    if args.test:
+        expected = run_torch(x, w, xs, ws, args.weight_group_rows)
+        torch.testing.assert_close(
+            run(),
+            expected.to(dtype),
+            rtol={"bf16": 0.016, "fp16": 0.002, "fp32": 3e-5}[args.dtype],
+            atol=5e-5 * expected.abs().max().item(),
+        )
+    ms = (
+        triton.testing.do_bench_cudagraph(run, rep=100)
+        if args.cudagraph
+        else triton.testing.do_bench(run, warmup=25, rep=100)
+    )
+    if metric == "time":
+        return ms
+    if metric == "throughput":
+        return 2.0 * m * n * k / ms * 1e-9
+    if metric == "bandwidth":
+        num_bytes = sum(t.numel() * t.element_size() for t in (x, w, xs, ws, y))
+        return num_bytes / ms * 1e-6
+    raise ValueError(f"Unknown metric: {metric}")
+
+
+def run_benchmark(args):
+    name = get_caller_name_no_ext()
+    benchmark = (
+        get_model_benchmark_object(name, args)
+        if args.model
+        else get_shape_benchmark_object(name, args)
+    )
+
+    @triton.testing.perf_report([benchmark])
+    def bench(
+        M,
+        metric,
+        N=None,
+        K=None,
+        hidden_dim=None,
+        intermediate_dim=None,
+        layer=None,
+        **kwargs,
+    ):
+        if args.model:
+            if layer == "fc1":
+                N = intermediate_dim * (1 if args.no_glu else 2)
+                K = hidden_dim
+            else:
+                N, K = hidden_dim, math.ceil(intermediate_dim / args.tp)
+        if not args.model or layer == "fc1":
+            N = math.ceil(N / args.tp)
+        return bench_gemm_fn(M, N, K, metric, args)
+
+    bench.run(save_path="." if args.o else None, print_data=True)
+
+
+def parse_args(argv=None):
+    parser = add_argparse_ff(get_parser(kernel_name="Native group32 A8W8 GEMM"))
+    parser.add_argument("--weight-group-rows", type=int, choices=(1, 32), default=32)
+    parser.add_argument("--dtype", choices=tuple(DTYPES), default="bf16")
+    parser.add_argument(
+        "--cudagraph", action="store_true", help="Time CUDA graph replay"
+    )
+    parser.add_argument(
+        "-test", action="store_true", help="Check against FP64 reference"
+    )
+    args, _ = get_ff_args(parser, args=argv)
+    if args.shape is not None and (len(args.shape) != 3 or args.model):
+        parser.error("Use --shape M N K or --model with an optional -M")
+    if args.layout != "TN":
+        parser.error("Native group32 weights require --layout TN")
+    if args.tp < 1:
+        parser.error("-tp must be positive")
+    return args
+
+
+def main(argv=None):
+    args = parse_args(argv)
+    if args.print_vgpr:
+        print_vgpr(lambda: run_benchmark(args), get_caller_name_no_ext())
+    else:
+        run_benchmark(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/op_tests/test_gemm_group32_interface.py b/op_tests/test_gemm_group32_interface.py
new file mode 100644
index 00000000000..bcfe8377e87
--- /dev/null
+++ b/op_tests/test_gemm_group32_interface.py
@@ -0,0 +1,275 @@
+# SPDX-License-Identifier: MIT
+# Copyright (C) 2026, Advanced Micro Devices, Inc. All rights reserved.
+"""CPU contracts for A8W8 format dispatch and JSON-defined GEMM bounds."""
+
+import json
+
+import pytest
+import torch
+
+from aiter.jit.core import AITER_CONFIGS
+from aiter.ops import gemm_op_a8w8
+from aiter.ops.triton.gemm.basic import gemm_a8w8_blockscale_group32 as group32
+from aiter.ops.triton.utils import gemm_config_utils
+from aiter.ops.triton.utils.config_utils import load_config_json
+
+
+@pytest.fixture
+def config_dir(tmp_path, monkeypatch):
+    monkeypatch.setattr(
+        gemm_config_utils, "resolve_config_dir", lambda *args, **kwargs: str(tmp_path)
+    )
+    gemm_config_utils._get_gemm_config_cached.cache_clear()
+    load_config_json.cache_clear()
+    yield tmp_path
+    gemm_config_utils._get_gemm_config_cached.cache_clear()
+    load_config_json.cache_clear()
+
+
+def test_file_bounds_and_explicit_override(config_dir):
+    table = {
+        "M_BOUNDS": [3, 7],
+        "M_LEQ_3": {"value": 3},
+        "M_LEQ_7": {"value": 7},
+        "any": {"value": 99},
+    }
+    (config_dir / "DEFAULT.json").write_text(json.dumps(table))
+    assert gemm_config_utils.get_gemm_config("GEMM-TEST", 2)[0]["value"] == 3
+    assert gemm_config_utils.get_gemm_config("GEMM-TEST", 4)[0]["value"] == 7
+    assert gemm_config_utils.get_gemm_config("GEMM-TEST", 8)[0]["value"] == 99
+    assert (
+        gemm_config_utils.get_gemm_config("GEMM-TEST", 2, bounds=(7,))[0]["value"] == 7
+    )
+
+
+def test_legacy_bounds_and_specialized_nested_copy(config_dir):
+    (config_dir / "DEFAULT.json").write_text(
+        json.dumps(
+            {"M_LEQ_3": {"value": 3}, "M_LEQ_4": {"value": 4}, "any": {"value": 99}}
+        )
+    )
+    assert gemm_config_utils.get_gemm_config("GEMM-TEST", 2)[0]["value"] == 4
+    (config_dir / "GEMM-TEST-N=64-K=32.json").write_text(
+        json.dumps(
+            {
+                "M_BOUNDS": [3],
+                "M_LEQ_3": {"packed": {"K_PACK": 4}, "nested": [{"value": 1}]},
+                "any": {"value": 99},
+            }
+        )
+    )
+    first, tuned = gemm_config_utils.get_gemm_config("GEMM-TEST", 2, 64, 32)
+    assert tuned
+    first["packed"]["K_PACK"] = 1
+    first["nested"][0]["value"] = 2
+    assert gemm_config_utils.get_gemm_config("GEMM-TEST", 2, 64, 32)[0]["nested"] == [
+        {"value": 1}
+    ]
+    assert (
+        gemm_config_utils.get_gemm_config("GEMM-TEST", 2, 64, 32)[0]["packed"]["K_PACK"]
+        == 4
+    )
+
+
+@pytest.mark.parametrize("bounds", [[], [4, 3], [3, 3], [0, 4], [1.5, 4], [True, 4]])
+def test_invalid_file_bounds(config_dir, bounds):
+    (config_dir / "DEFAULT.json").write_text(
+        json.dumps({"M_BOUNDS": bounds, "any": {"value": 1}})
+    )
+    with pytest.raises(AssertionError, match="M_BOUNDS"):
+        gemm_config_utils.get_gemm_config("GEMM-TEST", 2)
+
+
+@pytest.mark.parametrize("group_n", [1, 32])
+@pytest.mark.parametrize("libtype", [None, "triton", "ck", "cktile", "unknown"])
+@pytest.mark.parametrize("split_k", [None, 3])
+@pytest.mark.parametrize(
+    "scale_dtypes",
+    [
+        (torch.float8_e8m0fnu, torch.float8_e8m0fnu),
+        (torch.uint8, torch.uint8),
+        (torch.float8_e8m0fnu, torch.uint8),
+        (torch.uint8, torch.float8_e8m0fnu),
+    ],
+)
+def test_public_native_group32_route(
+    monkeypatch, group_n, libtype, split_k, scale_dtypes
+):
+    calls = []
+    expected = torch.empty((3, 65), dtype=torch.float32)
+
+    def backend(x, w, xs, ws, **kwargs):
+        calls.append(kwargs)
+        return expected
+
+    def lookup(m, n, k, tuned_file):
+        assert (m, n, k) == (3, 65, 64)
+        assert tuned_file.endswith("a8w8_blockscale_group32_tuned_gemm.csv")
+        calls.append("lookup")
+        return None if libtype is None else {"libtype": libtype}
+
+    monkeypatch.setattr(gemm_op_a8w8, "get_CKGEMM_config", lookup)
+    monkeypatch.setattr(group32, "gemm_a8w8_blockscale_group32", backend)
+    x = torch.empty((3, 64), dtype=torch.float8_e4m3fn)
+    w = torch.empty((65, 64), dtype=torch.float8_e4m3fn)
+    xs = torch.empty((3, 2), dtype=scale_dtypes[0])
+    ws = torch.empty(((65 + group_n - 1) // group_n, 2), dtype=scale_dtypes[1])
+    if libtype not in (None, "triton"):
+        with pytest.raises(AssertionError, match="Unsupported libtype"):
+            gemm_op_a8w8.gemm_a8w8_blockscale(
+                x, w, xs, ws, dtype=torch.float32, split_k=split_k
+            )
+        assert calls == ["lookup"]
+        return
+    actual = gemm_op_a8w8.gemm_a8w8_blockscale(
+        x, w, xs, ws, dtype=torch.float32, split_k=split_k
+    )
+    assert actual is expected
+    assert calls == [
+        "lookup",
+        {"dtype": torch.float32, "weight_group_rows": group_n, "split_k": split_k},
+    ]
+
+
+@pytest.mark.parametrize("libtype", ["ck", "cktile"])
+def test_public_legacy_ck_route_and_readonly_schema(monkeypatch, libtype):
+    calls = []
+
+    def ck(x, w, xs, ws, out, **kwargs):
+        calls.append(kwargs)
+        return out
+
+    monkeypatch.setattr(gemm_op_a8w8, "_hip_blockscale_supported", lambda: True)
+    monkeypatch.setattr(
+        gemm_op_a8w8,
+        "get_CKGEMM_config",
+        lambda *args: {"libtype": libtype, "splitK": 2, "kernelName": "existing"},
+    )
+    monkeypatch.setattr(gemm_op_a8w8, f"gemm_a8w8_blockscale_{libtype}", ck)
+    x = torch.empty((3, 128), dtype=torch.float8_e4m3fn)
+    w = torch.empty((128, 128), dtype=torch.float8_e4m3fn)
+    xs = torch.empty((3, 1), dtype=torch.float32)
+    ws = torch.empty((1, 1), dtype=torch.float32)
+    actual = gemm_op_a8w8.gemm_a8w8_blockscale(x, w, xs, ws)
+    assert actual.shape == (3, 128)
+    assert calls == [{"splitK": 2, "kernelName": "existing"}]
+    schema = torch.ops.aiter.gemm_a8w8_blockscale.default._schema
+    assert all(arg.alias_info is None for arg in schema.arguments)
+
+
+@pytest.fixture
+def backend_config_files(tmp_path, monkeypatch):
+    monkeypatch.setattr(gemm_op_a8w8, "get_gfx", lambda: "gfx950")
+    monkeypatch.setattr(gemm_op_a8w8, "get_cu_num", lambda: 256)
+    monkeypatch.setattr(gemm_op_a8w8, "_CKGEMM_CONFIG_CACHE", {})
+    monkeypatch.setattr(gemm_op_a8w8, "_CKGEMM_HAS_GFX", {})
+    gemm_op_a8w8.get_CKGEMM_config.cache_clear()
+    AITER_CONFIGS.get_config_file.cache_clear()
+    files = {}
+    for suffix in ("", "_GROUP32"):
+        path = tmp_path / f"blockscale{suffix.lower()}.csv"
+        monkeypatch.setenv(f"AITER_CONFIG_GEMM_A8W8_BLOCKSCALE{suffix}", str(path))
+        files[suffix] = path
+    yield files
+    gemm_op_a8w8.get_CKGEMM_config.cache_clear()
+    AITER_CONFIGS.get_config_file.cache_clear()
+
+
+@pytest.mark.parametrize("libtype", [None, "triton", "unknown"])
+def test_scale_formats_have_independent_backend_configs(
+    backend_config_files, monkeypatch, libtype
+):
+    # Identical M/N/K must not make the FP32 128x128 winner apply to E8M0.
+    header = "gfx,cu_num,M,N,K,libtype,splitK,kernelName\n"
+    backend_config_files[""].write_text(header + "gfx950,256,4,128,128,ck,2,legacy\n")
+    backend_config_files["_GROUP32"].write_text(
+        header
+        + ("" if libtype is None else f"gfx950,256,4,128,128,{libtype},0,native\n")
+    )
+    calls = []
+
+    def ck(x, w, xs, ws, out, **kwargs):
+        assert xs.dtype == ws.dtype == torch.float32
+        assert kwargs == {"splitK": 2, "kernelName": "legacy"}
+        calls.append("ck")
+        return out
+
+    def native(x, w, xs, ws, **kwargs):
+        assert xs.dtype == ws.dtype == torch.float8_e8m0fnu
+        calls.append("group32")
+        return torch.empty((4, 128), dtype=kwargs["dtype"])
+
+    monkeypatch.setattr(gemm_op_a8w8, "gemm_a8w8_blockscale_ck", ck)
+    monkeypatch.setattr(group32, "gemm_a8w8_blockscale_group32", native)
+    x = torch.empty((4, 128), dtype=torch.float8_e4m3fn)
+    w = torch.empty((128, 128), dtype=torch.float8_e4m3fn)
+    gemm_op_a8w8.gemm_a8w8_blockscale(x, w, torch.empty((4, 1)), torch.empty((1, 1)))
+    assert calls == ["ck"]
+    xs = torch.empty((4, 4), dtype=torch.float8_e8m0fnu)
+    ws = torch.empty((4, 4), dtype=torch.float8_e8m0fnu)
+    if libtype == "unknown":
+        with pytest.raises(AssertionError, match="Unsupported libtype unknown"):
+            gemm_op_a8w8.gemm_a8w8_blockscale(x, w, xs, ws)
+        assert calls == ["ck"]
+    else:
+        gemm_op_a8w8.gemm_a8w8_blockscale(x, w, xs, ws)
+        assert calls == ["ck", "group32"]
+
+
+@pytest.mark.parametrize("configured", [False, True])
+def test_legacy_triton_config_and_fallback(monkeypatch, configured):
+    calls = []
+
+    def lookup(*args):
+        calls.append("lookup")
+        return {"libtype": "triton"} if configured else None
+
+    def triton(x, w, xs, ws, dtype, **kwargs):
+        calls.append("triton")
+        return torch.empty((3, 128), dtype=dtype)
+
+    monkeypatch.setattr(gemm_op_a8w8, "get_CKGEMM_config", lookup)
+    monkeypatch.setattr(gemm_op_a8w8, "_hip_blockscale_supported", lambda: False)
+    monkeypatch.setattr(gemm_op_a8w8, "_blockscale_triton", triton)
+    gemm_op_a8w8.gemm_a8w8_blockscale(
+        torch.empty((3, 128), dtype=torch.float8_e4m3fn),
+        torch.empty((128, 128), dtype=torch.float8_e4m3fn),
+        torch.empty((3, 1)),
+        torch.empty((1, 1)),
+    )
+    assert calls == ["lookup", "triton"]
+
+
+def test_legacy_scale_format_rejects_native_split_override():
+    with pytest.raises(
+        AssertionError, match="split_k override requires native group32"
+    ):
+        gemm_op_a8w8.gemm_a8w8_blockscale(
+            torch.empty((3, 128), dtype=torch.float8_e4m3fn),
+            torch.empty((128, 128), dtype=torch.float8_e4m3fn),
+            torch.empty((3, 1)),
+            torch.empty((1, 1)),
+            split_k=3,
+        )
+
+
+@pytest.mark.parametrize(
+    "invalid", ["activation_groups", "weight_groups", "mixed_formats"]
+)
+def test_invalid_byte_scales_never_reach_legacy_dispatch(monkeypatch, invalid):
+    def lookup(*args):
+        pytest.fail("Invalid byte scales reached backend configuration lookup")
+
+    monkeypatch.setattr(gemm_op_a8w8, "get_CKGEMM_config", lookup)
+    x = torch.empty((3, 128), dtype=torch.float8_e4m3fn)
+    w = torch.empty((65, 128), dtype=torch.float8_e4m3fn)
+    xs = torch.empty((3, 4), dtype=torch.uint8)
+    ws = torch.empty((3, 4), dtype=torch.uint8)
+    if invalid == "activation_groups":
+        xs = torch.empty((3, 1), dtype=torch.uint8)
+    elif invalid == "weight_groups":
+        ws = torch.empty((3, 1), dtype=torch.uint8)
+    else:
+        xs = xs.float()
+    with pytest.raises(AssertionError, match="Expected E8M0 group32 scale shapes"):
+        gemm_op_a8w8.gemm_a8w8_blockscale(x, w, xs, ws)
diff --git a/op_tests/triton_tests/gemm/basic/test_gemm_a8w8_blockscale_group32.py b/op_tests/triton_tests/gemm/basic/test_gemm_a8w8_blockscale_group32.py
new file mode 100644
index 00000000000..f58c1c68f54
--- /dev/null
+++ b/op_tests/triton_tests/gemm/basic/test_gemm_a8w8_blockscale_group32.py
@@ -0,0 +1,447 @@
+# SPDX-License-Identifier: MIT
+# Copyright (C) 2026, Advanced Micro Devices, Inc. All rights reserved.
+"""Numerical and replay contracts for native group32 FP8 projections."""
+
+import pytest
+import torch
+
+if not torch.cuda.is_available():
+    pytest.skip("ROCm GPU required", allow_module_level=True)
+
+from aiter.jit.utils.chip_info import get_gfx
+from aiter.ops import gemm_op_a8w8
+from aiter.ops.gemm_op_a8w8 import gemm_a8w8_blockscale
+from aiter.ops.triton.gemm.basic import gemm_a8w8_blockscale_group32 as group32_op
+from aiter.ops.triton.gemm.basic.gemm_a8w8_blockscale_group32 import (
+    gemm_a8w8_blockscale_group32,
+)
+from aiter.ops.triton.utils.gemm_config_utils import get_gemm_config
+from aiter.test_common import checkAllclose
+
+pytestmark = pytest.mark.skipif(get_gfx() != "gfx950", reason="CDNA4 packed MFMA")
+
+
+def generate_inputs(m, n, k, weight_group_rows=32):
+    torch.manual_seed(m + n + k)
+    x = torch.randn(m, k, device="cuda").to(torch.float8_e4m3fn)
+    weight = torch.randn(n, k, device="cuda").to(torch.float8_e4m3fn)
+    xs = torch.randint(122, 131, (m, k // 32), device="cuda", dtype=torch.uint8)
+    ws = torch.randint(
+        122,
+        131,
+        (-(-n // weight_group_rows), k // 32),
+        device="cuda",
+        dtype=torch.uint8,
+    )
+    return x, weight, xs.view(torch.float8_e8m0fnu), ws.view(torch.float8_e8m0fnu)
+
+
+def run_torch(x, weight, xs, ws, weight_group_rows=32):
+    # FP64 is independent of the MFMA's internal block accumulation and of
+    # either implementation's K reduction order.
+    a = x.double() * xs.double().repeat_interleave(32, -1)
+    b = weight.double() * ws.double().repeat_interleave(weight_group_rows, 0)[
+        : weight.shape[0]
+    ].repeat_interleave(32, -1)
+    return a @ b.T
+
+
+@pytest.mark.parametrize(
+    "m,n,k",
+    [
+        (1, 5120, 576),
+        (3, 2053, 1280),
+        (4, 8192, 1280),
+        (8, 5120, 1152),
+        (16, 4096, 1280),
+        (31, 4096, 1280),
+        (3, 5120, 2048),
+        (8, 5120, 2304),
+        (4, 5120, 4096),
+        (3, 2304, 5120),
+        (1, 5120, 8192),
+        # Untuned geometries exercise the default configuration with tails.
+        (63, 8193, 1280),
+        (129, 4097, 576),
+        (255, 16385, 1152),
+        (1023, 4097, 1280),
+    ],
+)
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32])
+def test_group32_projection_panel_scales_and_tails(m, n, k, dtype):
+    x, weight, xs, ws = generate_inputs(m, n, k)
+    actual = gemm_a8w8_blockscale_group32(x, weight, xs, ws, dtype=dtype)
+    expected = run_torch(x, weight, xs, ws)
+    peak = expected.abs().max().item()
+    # Keep the established native-MFMA FP32 bound. BF16 additionally rounds
+    # output; near cancellation still uses the same peak-relative floor.
+    torch.testing.assert_close(
+        actual.float(),
+        expected.to(dtype).float(),
+        rtol=(
+            0.016
+            if dtype == torch.bfloat16
+            else 0.002 if dtype == torch.float16 else 3e-5
+        ),
+        atol=5e-5 * peak,
+    )
+
+
+@pytest.mark.parametrize("m,packed", [(3, True), (129, False)])
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32])
+def test_tuned_variants_use_production_config(m, packed, dtype):
+    n, k = 8192, 1280
+    config, tuned = get_gemm_config("GEMM-A8W8_BLOCKSCALE_GROUP32", m, n, k)
+    assert tuned
+    assert ("packed" in config) == packed
+    if not packed:
+        assert config["N_FIRST"]
+    x, w, xs, ws = generate_inputs(m, n, k)
+    actual = gemm_a8w8_blockscale_group32(x, w, xs, ws, dtype=dtype)
+    expected = run_torch(x, w, xs, ws)
+    torch.testing.assert_close(
+        actual.float(),
+        expected.to(dtype).float(),
+        rtol=(
+            0.016
+            if dtype == torch.bfloat16
+            else 0.002 if dtype == torch.float16 else 3e-5
+        ),
+        atol=5e-5 * expected.abs().max().item(),
+    )
+
+
+@pytest.mark.parametrize(
+    "a_code,b_code", [(0, 254), (254, 0), (128, 0), (255, 127), (127, 255)]
+)
+def test_group32_projection_extreme_scale_codes(a_code, b_code):
+    x = torch.ones(3, 1280, device="cuda").to(torch.float8_e4m3fn)
+    weight = torch.ones(2048, 1280, device="cuda").to(torch.float8_e4m3fn)
+    xs = torch.full((3, 40), a_code, device="cuda", dtype=torch.uint8).view(
+        torch.float8_e8m0fnu
+    )
+    ws = torch.full((64, 40), b_code, device="cuda", dtype=torch.uint8).view(
+        torch.float8_e8m0fnu
+    )
+    actual = gemm_a8w8_blockscale_group32(x, weight, xs, ws, dtype=torch.float32)
+    if 255 in (a_code, b_code):
+        assert actual.isnan().all()
+    else:
+        expected = torch.full_like(actual, 1280 * 2.0 ** (a_code + b_code - 254))
+        torch.testing.assert_close(actual, expected, rtol=0, atol=0)
+
+
+@pytest.mark.parametrize(
+    "rows,n,k,split_k", [(3, 4096, 1280, None), (33, 8193, 576, 3)]
+)
+def test_group32_projection_graph_reads_live_inputs_and_scales(rows, n, k, split_k):
+    x, weight, xs, ws = generate_inputs(2 * rows, n, k)
+    gemm_a8w8_blockscale_group32(
+        x, weight, xs, ws, dtype=torch.float32, split_k=split_k
+    )
+    torch.cuda.synchronize()
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        actual = gemm_a8w8_blockscale_group32(
+            x, weight, xs, ws, dtype=torch.float32, split_k=split_k
+        )
+    for factor in (2, 0.5):
+        x.copy_((x.float() * factor).to(x.dtype))
+        xs.view(torch.uint8).add_(1)
+        graph.replay()
+        expected = run_torch(x, weight, xs, ws)
+        torch.testing.assert_close(
+            actual.float(),
+            expected.float(),
+            rtol=3e-5,
+            atol=5e-5 * expected.abs().max().item(),
+        )
+
+
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32])
+def test_row_scaled_fp8_projection_with_token_and_column_tails(dtype):
+    x, weight, xs, ws = generate_inputs(97, 8193, 576)
+    # Per-row scales share the scheduling kernel but not the compact weight grid.
+    ws = ws.repeat_interleave(32, 0)[: weight.shape[0]].contiguous()
+    actual = gemm_a8w8_blockscale_group32(
+        x, weight, xs, ws, weight_group_rows=1, dtype=dtype, split_k=3
+    )
+    expected = (x.double() * xs.double().repeat_interleave(32, -1)) @ (
+        weight.double() * ws.double().repeat_interleave(32, -1)
+    ).T
+    torch.testing.assert_close(
+        actual.float(),
+        expected.to(dtype).float(),
+        rtol=(
+            0.016
+            if dtype == torch.bfloat16
+            else 0.002 if dtype == torch.float16 else 3e-5
+        ),
+        atol=5e-5 * expected.abs().max().item(),
+    )
+
+
+@pytest.mark.parametrize(
+    "m,n,k", [(0, 65, 64), (3, 2053, 1280), (66, 8193, 576), (512, 4096, 1280)]
+)
+@pytest.mark.parametrize("group_n", [1, 32])
+@pytest.mark.parametrize("configured", [False, True])
+@pytest.mark.parametrize("raw_views", [False, True])
+def test_public_group32_dispatch(m, n, k, group_n, configured, raw_views, monkeypatch):
+    if configured:
+        monkeypatch.setattr(
+            gemm_op_a8w8, "get_CKGEMM_config", lambda *args: {"libtype": "triton"}
+        )
+    x, w, xs, ws = generate_inputs(m, n, k)
+    if group_n == 1:
+        ws = ws.repeat_interleave(32, 0)[:n].contiguous()
+    expected = gemm_a8w8_blockscale_group32(x, w, xs, ws, weight_group_rows=group_n)
+    operands = (x, w, xs, ws)
+    if raw_views:
+        operands = tuple(t.view(torch.uint8) for t in operands)
+    actual = gemm_a8w8_blockscale(*operands)
+    torch.testing.assert_close(actual, expected, rtol=0, atol=0)
+
+
+@pytest.mark.parametrize("split_k", [None, 1, 3, 100])
+def test_raw_views_and_preallocated_output(split_k):
+    x, w, xs, ws = generate_inputs(63, 8193, 576)
+    output = torch.empty((63, 8193), dtype=torch.bfloat16, device="cuda")
+    actual = gemm_a8w8_blockscale_group32(
+        x.view(torch.uint8),
+        w.view(torch.uint8),
+        xs.view(torch.uint8),
+        ws.view(torch.uint8),
+        y=output,
+        split_k=split_k,
+    )
+    assert actual is output
+    expected = run_torch(x, w, xs, ws).to(output.dtype)
+    checkAllclose(
+        expected,
+        actual,
+        rtol=0.016,
+        atol=5e-5 * expected.abs().max().item(),
+        catastrophic_check=True,
+    )
+
+
+@pytest.mark.parametrize(
+    "invalid", ["stride", "scale_shape", "scale_dtype", "output", "split"]
+)
+def test_invalid_group32_contract(invalid):
+    x, w, xs, ws = generate_inputs(3, 2053, 1280)
+    kwargs = {}
+    if invalid == "stride":
+        x = x.T.contiguous().T
+    elif invalid == "scale_shape":
+        ws = ws[:-1]
+    elif invalid == "scale_dtype":
+        xs = xs.float()
+    elif invalid == "output":
+        kwargs["y"] = torch.empty((3, 2052), dtype=torch.bfloat16, device="cuda")
+    else:
+        kwargs["split_k"] = 0
+    with pytest.raises(AssertionError):
+        gemm_a8w8_blockscale_group32(x, w, xs, ws, **kwargs)
+
+
+@pytest.mark.parametrize("split_k", [None, 3])
+def test_public_group32_compile_dynamic_rows(split_k):
+    def forward(x, w, xs, ws):
+        return gemm_a8w8_blockscale(x, w, xs, ws, split_k=split_k)
+
+    compiled = torch.compile(forward, fullgraph=True, dynamic=True)
+    for m in (3, 7, 65):
+        x, w, xs, ws = generate_inputs(m, 4096, 1280)
+        torch.testing.assert_close(
+            compiled(x, w, xs, ws), forward(x, w, xs, ws), rtol=0, atol=0
+        )
+
+
+@pytest.mark.parametrize("configured", [False, True])
+@pytest.mark.parametrize("split_k", [None, 3])
+def test_public_group32_graph_replay(configured, split_k, monkeypatch):
+    if configured:
+        monkeypatch.setattr(
+            gemm_op_a8w8, "get_CKGEMM_config", lambda *args: {"libtype": "triton"}
+        )
+    x, w, xs, ws = generate_inputs(3, 4096, 1280)
+    gemm_a8w8_blockscale(x, w, xs, ws, split_k=split_k)
+    torch.cuda.synchronize()
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        actual = gemm_a8w8_blockscale(x, w, xs, ws, split_k=split_k)
+    x.copy_((x.float() * 0.5).to(x.dtype))
+    xs.view(torch.uint8).add_(1)
+    graph.replay()
+    expected = gemm_a8w8_blockscale_group32(x, w, xs, ws, split_k=split_k)
+    torch.testing.assert_close(actual, expected, rtol=0, atol=0)
+
+
+@pytest.mark.parametrize("n", [1, 2, 16, 31, 32, 33])
+@pytest.mark.parametrize("group_n", [1, 32])
+@pytest.mark.parametrize("raw_views", [False, True])
+def test_public_small_n_scale_layouts(n, group_n, raw_views):
+    x, w, xs, ws = generate_inputs(3, n, 128, weight_group_rows=group_n)
+    expected = run_torch(x, w, xs, ws, weight_group_rows=group_n)
+    operands = (x, w, xs, ws)
+    if raw_views:
+        operands = tuple(t.view(torch.uint8) for t in operands)
+    actual = gemm_a8w8_blockscale(*operands, dtype=torch.float32)
+    torch.testing.assert_close(
+        actual.double(), expected, rtol=3e-5, atol=5e-5 * expected.abs().max().item()
+    )
+
+
+_FUSED_TILE = {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "num_warps": 2,
+    "num_stages": 2,
+    "waves_per_eu": 0,
+    "matrix_instr_nonkdim": 16,
+    "NUM_KSPLIT": 5,
+    "N_FIRST": False,
+    "REDUCE_BLOCK_SIZE_M": 32,
+    "REDUCE_BLOCK_SIZE_N": 32,
+    "FUSED_SPLITK": True,
+}
+_FUSED_PACKED = dict(
+    _FUSED_TILE,
+    packed={
+        "BLOCK_SIZE_M": 8,
+        "BLOCK_SIZE_N": 16,
+        "BLOCK_SIZE_K": 256,
+        "K_PACK": 2,
+        "NUM_KSPLIT": 5,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0,
+        "matrix_instr_nonkdim": 16,
+    },
+)
+_FUSED = {"tile": _FUSED_TILE, "packed": _FUSED_PACKED}
+
+
+def _assert_matches_reference(actual, x, w, xs, ws, dtype, weight_group_rows=32):
+    expected = run_torch(x, w, xs, ws, weight_group_rows)
+    torch.testing.assert_close(
+        actual.float(),
+        expected.to(dtype).float(),
+        rtol=(
+            0.016
+            if dtype == torch.bfloat16
+            else 0.002 if dtype == torch.float16 else 3e-5
+        ),
+        atol=5e-5 * expected.abs().max().item(),
+    )
+
+
+@pytest.mark.parametrize("variant", ["tile", "packed"])
+@pytest.mark.parametrize(
+    "m,n,k", [(1, 512, 5120), (3, 2053, 1280), (16, 1152, 5120), (37, 5120, 2304)]
+)
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32])
+def test_fused_split_k_matches_reference(variant, m, n, k, dtype):
+    x, w, xs, ws = generate_inputs(m, n, k)
+    actual = gemm_a8w8_blockscale_group32(
+        x, w, xs, ws, dtype=dtype, config=_FUSED[variant]
+    )
+    _assert_matches_reference(actual, x, w, xs, ws, dtype)
+
+
+@pytest.mark.parametrize("variant", ["tile", "packed"])
+@pytest.mark.parametrize("m,n,k", [(3, 2053, 1280), (37, 5120, 2336)])
+def test_weight_cache_modifier_keeps_results(variant, m, n, k):
+    x, w, xs, ws = generate_inputs(m, n, k)
+    config = _FUSED[variant]
+    cached = dict(config, cache_modifier=".cg")
+    if variant == "packed":
+        cached["packed"] = dict(config["packed"], cache_modifier=".cg")
+    run = gemm_a8w8_blockscale_group32
+    expected = run(x, w, xs, ws, dtype=torch.float32, config=config)
+    actual = run(x, w, xs, ws, dtype=torch.float32, config=cached)
+    torch.testing.assert_close(actual, expected, rtol=0, atol=0)
+
+
+def test_fused_split_k_row_scales():
+    x, w, xs, ws = generate_inputs(5, 2053, 1280, weight_group_rows=1)
+    actual = gemm_a8w8_blockscale_group32(
+        x, w, xs, ws, weight_group_rows=1, dtype=torch.float32, config=_FUSED_TILE
+    )
+    _assert_matches_reference(actual, x, w, xs, ws, torch.float32, 1)
+
+
+@pytest.mark.parametrize("variant", ["tile", "packed"])
+def test_fused_split_k_is_deterministic_and_rezeroes_counters(variant):
+    x, w, xs, ws = generate_inputs(4, 1152, 5120)
+    run = lambda: gemm_a8w8_blockscale_group32(
+        x, w, xs, ws, dtype=torch.float32, config=_FUSED[variant]
+    )
+    first = run()
+    # A stale counter or an early partial read would change a later sum.
+    assert all(torch.equal(first, run()) for _ in range(50))
+    torch.cuda.synchronize()
+    assert not group32_op._split_counters(x.device).any()
+
+
+@pytest.mark.parametrize("variant", ["tile", "packed"])
+def test_fused_split_k_graph_replay_on_warmed_stream(variant):
+    x, w, xs, ws = generate_inputs(6, 1152, 5120)
+    stream = torch.cuda.Stream()
+    with torch.cuda.stream(stream):
+        gemm_a8w8_blockscale_group32(
+            x, w, xs, ws, dtype=torch.float32, config=_FUSED[variant]
+        )
+    stream.synchronize()
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph, stream=stream):
+        actual = gemm_a8w8_blockscale_group32(
+            x, w, xs, ws, dtype=torch.float32, config=_FUSED[variant]
+        )
+    for factor in (2, 0.5):
+        x.copy_((x.float() * factor).to(x.dtype))
+        xs.view(torch.uint8).add_(1)
+        graph.replay()
+        torch.cuda.synchronize()
+        _assert_matches_reference(actual, x, w, xs, ws, torch.float32)
+
+
+@pytest.mark.parametrize("variant", ["tile", "packed"])
+def test_fused_split_k_first_use_inside_capture(variant):
+    # Before torch 2.10 counters cannot be allocated mid-capture: runs unfused.
+    x, w, xs, ws = generate_inputs(6, 1152, 5120)
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        actual = gemm_a8w8_blockscale_group32(
+            x, w, xs, ws, dtype=torch.float32, config=_FUSED[variant]
+        )
+    graph.replay()
+    torch.cuda.synchronize()
+    _assert_matches_reference(actual, x, w, xs, ws, torch.float32)
+
+
+@pytest.mark.parametrize("variant", ["tile", "packed"])
+def test_fused_split_k_concurrent_streams(variant):
+    inputs = [generate_inputs(m, 1152, 5120) for m in (3, 5)]
+    expected = [
+        gemm_a8w8_blockscale_group32(*t, dtype=torch.float32, config=_FUSED[variant])
+        for t in inputs
+    ]
+    torch.cuda.synchronize()
+    streams = [torch.cuda.Stream() for _ in inputs]
+    outputs = [[], []]
+    # Unsynchronized interleaving keeps both streams' reductions in flight.
+    for _ in range(40):
+        for i, (stream, operands) in enumerate(zip(streams, inputs)):
+            with torch.cuda.stream(stream):
+                outputs[i].append(
+                    gemm_a8w8_blockscale_group32(
+                        *operands, dtype=torch.float32, config=_FUSED[variant]
+                    )
+                )
+    torch.cuda.synchronize()
+    for want, got in zip(expected, outputs):
+        assert all(torch.equal(want, y) for y in got)