diff --git a/python/triton_kernels/tests/test_matmul.py b/python/triton_kernels/tests/test_matmul.py
index 787118d37e45..6cbe32d7291d 100644
--- a/python/triton_kernels/tests/test_matmul.py
+++ b/python/triton_kernels/tests/test_matmul.py
@@ -127,6 +127,10 @@ def _build_test_op_cases():
         Case(*even_shape, "ragged", "float8_e5m2", "float8_e5m2", epilogue_subtile=val)
         for val in (1, 2, 4)
     ])
+    # fp32
+    test_cases.extend([
+        Case(1024, 1000, 2048, "ragged", "float32", "float32", b_transpose=True)
+    ])
     # bfloat16 x mx
     for shape in [odd_shape2, even_shape]:
         test_cases.extend([
@@ -281,6 +285,8 @@ def _test_op(m, n, k, split_k, do_gather, do_scatter, inner_expt_opt, do_gamma,
             pytest.skip("NYI: gamma and swiglu not supported together on AMD GPU")
         if split_k is not None and split_k > 1:
             pytest.skip("splitK hasn't been fully tested on AMD GPU.")
+        if "float32" in act_dtype_str:
+            pytest.skip("float32 not fully tested on AMD GPU")
 
     if "float8_e4m3fnuz" in (weight_dtype_str, act_dtype_str) and not is_hip_cdna3():
         pytest.skip("float8_e4m3fnuz only tested on AMD CDNA3 Platform")
diff --git a/python/triton_kernels/triton_kernels/matmul.py b/python/triton_kernels/triton_kernels/matmul.py
index b769ae92c052..7b85ddf66a58 100644
--- a/python/triton_kernels/triton_kernels/matmul.py
+++ b/python/triton_kernels/triton_kernels/matmul.py
@@ -386,12 +386,16 @@ def matmul(a, b, bias,
     block_k = None
     if ragged_dimension == "K":
         block_k = a_ragged_metadata.slice_sizes_divisibility or b_ragged_metadata.slice_sizes_divisibility
+        a_uses_tma_when_persistent = a.stride(-1) != 1 or (a_ragged_metadata.slice_sizes_divisibility is not None)
+    else:
+        a_uses_tma_when_persistent = has_gather_tma or not has_gather
     opt_flags = make_opt_flags(out_dtype, a.dtype, b.dtype, precision_config,
         batch_size, M, N, b.shape[-2], a_ragged_metadata,
         can_use_tma, can_use_split_k, epilogue.effective_itemsize,
         a_transpose, c_acc_in is not None,
         block_k = block_k,
         mx_block_size = mx_block_size,
+        x_uses_tma_when_persistent = a_uses_tma_when_persistent,
     )
     # there seems to be a bug on A100
     # pytest -vs test_matmul.py::test_op[False-False-False-False-pad_b-16-768-512-1024-ragged-float16-float16-10-1-False-None-False-False-False-True-None]
@@ -485,6 +489,7 @@ def matmul(a, b, bias,
     # create tma descriptor for y
     c_has_tma = (
         opt_flags.is_persistent and (scatter_indx is None or has_scatter_tma)
+        and is_tma_compliant(c)
         and (c_acc_in is None or c_acc_is_c)
         and fused_comm is None
         and precision_config.c_value_pack_factor == 1
diff --git a/python/triton_kernels/triton_kernels/matmul_details/opt_flags.py b/python/triton_kernels/triton_kernels/matmul_details/opt_flags.py
index b6d185829183..736c8f690413 100644
--- a/python/triton_kernels/triton_kernels/matmul_details/opt_flags.py
+++ b/python/triton_kernels/triton_kernels/matmul_details/opt_flags.py
@@ -190,6 +190,7 @@ def make_default_opt_flags_nvidia(
     x_transpose,
     has_y_acc_in,
     constraints,
+    x_uses_tma_when_persistent=True,
     mx_block_size=None,
 ):
     constraints_supported = {"block_m", "block_n", "block_k", "split_k", "is_persistent", "epilogue_subtile", "num_stages", "idle_sms", "max_allowable_mn", "num_warps"}
@@ -267,6 +268,13 @@ def _is_layout_strided(layout: Layout | None) -> bool:
 
     # adjust block_n based on is_persistent signal
     block_n = block_n_tma if is_persistent else block_n
+    if (is_persistent and constraints.get("block_n", None) is None
+            and cuda_capability_geq(10, 0) and (lhs_dtype == FP32 or rhs_dtype == FP32)
+            and not x_uses_tma_when_persistent):
+        # Blackwell's fp32/tf32 persistent dot stages an operand in TMEM in
+        # addition to the accumulator. A 128x256 accumulator already consumes
+        # the full 512-column TMEM budget, so leave headroom for that operand.
+        block_n = min(block_n, 128)
     # adjust block_m based on is_persistent signal
     if is_persistent and opt_flags_nvidia.is_x_scale_swizzled(precision_config):
         # a mx scale has been swizzled to BlackwellActMXScaleLayout, enforce block_m=128 to align with swizzling layout
@@ -404,6 +412,7 @@ def make_opt_flags(
     has_y_acc_in,
     block_k,
     mx_block_size=None,
+    x_uses_tma_when_persistent=True,
 ):
     if _opt_flags_constraints.get("is_persistent", False) and not can_use_persistent_tma:
         raise InapplicableConstraint("cannot enforce `is_persistent=True` constraint")
@@ -429,5 +438,9 @@ def make_opt_flags(
     if backend == "hip":
         return make_default_opt_flags_amd(*args)
     if backend == "cuda":
-        return make_default_opt_flags_nvidia(*args, mx_block_size=mx_block_size)
+        return make_default_opt_flags_nvidia(
+            *args,
+            x_uses_tma_when_persistent=x_uses_tma_when_persistent,
+            mx_block_size=mx_block_size,
+        )
     assert False