diff --git a/python/sglang/multimodal_gen/envs.py b/python/sglang/multimodal_gen/envs.py
index 0a0ee5b0e92f..c16f9e7cc32c 100644
--- a/python/sglang/multimodal_gen/envs.py
+++ b/python/sglang/multimodal_gen/envs.py
@@ -108,6 +108,7 @@
     SGLANG_USE_ROCM_CUDNN_BENCHMARK: bool = False
     SGLANG_USE_ROCM_VAE_CONV2D: bool = False
     SGLANG_USE_ROCM_VAE_CONV2D_BF16: bool = False
+    SGLANG_USE_ROCM_AITER_TGEMM: bool = False
 
 
 def get_default_cache_root() -> str:
@@ -485,6 +486,9 @@ def _getter():
     "SGLANG_USE_ROCM_VAE_CONV2D": _lazy_bool("SGLANG_USE_ROCM_VAE_CONV2D"),
     # ROCm: use BF16 compute for the Conv2D replacement (implies CONV2D=true)
     "SGLANG_USE_ROCM_VAE_CONV2D_BF16": _lazy_bool("SGLANG_USE_ROCM_VAE_CONV2D_BF16"),
+    # ROCm: route unquantized bf16/fp16 linears through aiter tgemm, which picks
+    # the backend per (M, N, K) from aiter's bf16_tuned_gemm CSVs
+    "SGLANG_USE_ROCM_AITER_TGEMM": _lazy_bool("SGLANG_USE_ROCM_AITER_TGEMM"),
 }
 
 # Add cache-dit Secondary Transformer Env Vars via programmatic generation to reduce duplication
diff --git a/python/sglang/multimodal_gen/runtime/layers/linear.py b/python/sglang/multimodal_gen/runtime/layers/linear.py
index ec8ae6b68a98..ff7884ed020b 100644
--- a/python/sglang/multimodal_gen/runtime/layers/linear.py
+++ b/python/sglang/multimodal_gen/runtime/layers/linear.py
@@ -11,6 +11,7 @@
 from torch.nn.parameter import Parameter
 
 from sglang.kernels.kernel_api_logging import wrap_method_with_debug_kernel_once
+from sglang.multimodal_gen import envs
 from sglang.multimodal_gen.runtime.distributed import (
     divide,
     get_tp_group,
@@ -42,6 +43,11 @@
 logger = init_logger(__name__)
 
 IS_AMP_SUPPORTED = current_platform.is_amp_supported()
+_USE_AITER_TGEMM = current_platform.is_rocm() and envs.SGLANG_USE_ROCM_AITER_TGEMM
+if _USE_AITER_TGEMM:
+    from aiter.tuned_gemm import tgemm
+# aiter gemm_a16w16 kernels index operands with int32 offsets and fault beyond this.
+_AITER_TGEMM_MAX_NUMEL = 2**31
 WEIGHT_LOADER_V2_SUPPORTED = [
     "CompressedTensorsLinearMethod",
     "AWQMarlinLinearMethod",
@@ -144,6 +150,16 @@ def apply_unquantized_linear(
             None if bias is None else bias.to(torch.float32),
         ).to(x.dtype)
 
+    if (
+        _USE_AITER_TGEMM
+        and x.dtype in (torch.bfloat16, torch.float16)
+        and weight.dtype == x.dtype
+        and (bias is None or bias.dtype == x.dtype)
+        and x.numel() < _AITER_TGEMM_MAX_NUMEL
+        and x.numel() // x.shape[-1] * weight.shape[0] < _AITER_TGEMM_MAX_NUMEL
+    ):
+        return tgemm.mm(x, weight, bias)
+
     return (
         F.linear(x, weight, bias)
         if IS_AMP_SUPPORTED or bias is None