[Feature] Add SM103 (Blackwell Ultra) Support to vLLM (#30484)

Signed-off-by: LopezCastroRoberto <robertol.c510@gmail.com>
Signed-off-by: Roberto L. Castro <38211239+LopezCastroRoberto@users.noreply.github.com>
Co-authored-by: youkaichao <youkaichao@gmail.com>

tests/compile/distributed/test_fusions_e2e.py

@@ -20,7 +20,7 @@ from vllm.utils.torch_utils import is_torch_equal_or_newer
 
 from ...utils import flat_product, multi_gpu_test
 
-is_blackwell = lambda: current_platform.is_device_capability(100)
+is_blackwell = lambda: current_platform.is_device_capability_family(100)
 """Are we running on Blackwell, a lot of tests depend on it"""
 
 

tests/kernels/attention/test_cutlass_mla_decode.py

@@ -32,8 +32,8 @@ def cal_diff(
 
 
 CUTLASS_MLA_UNSUPPORTED_REASON = (
-    "Cutlass MLA Requires compute capability of 10 or above."
+    "Cutlass MLA Requires compute capability of 100 or above."
-    if not current_platform.is_device_capability(100)
+    if not current_platform.is_device_capability_family(100)
     else "Cutlass MLA is supported"
 )
 

tests/kernels/attention/test_flashinfer_trtllm_attention.py

@@ -11,7 +11,7 @@ from tests.kernels.quantization.nvfp4_utils import (
 from vllm.platforms import current_platform
 from vllm.utils.math_utils import round_up
 
-if not current_platform.is_device_capability(100):
+if not current_platform.is_device_capability_family(100):
     pytest.skip(
         "This TRTLLM kernel requires NVIDIA Blackwell.", allow_module_level=True
     )
@@ -443,7 +443,7 @@ def test_flashinfer_trtllm_prefill_with_baseline(
         output_trtllm = output_trtllm.reshape(-1, query.shape[1], query.shape[2])
 
     if q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP4_DTYPE:
-        rtol, atol = 1e-1, 2e-1
+        rtol, atol = 3e-1, 4e-1
     elif q_quant_dtype == FP8_DTYPE and o_quant_dtype == FP8_DTYPE:
         rtol, atol = 4e-2, 6e-2
     elif q_quant_dtype == FP8_DTYPE and o_quant_dtype == dtype:

tests/kernels/moe/test_ocp_mx_moe.py

@@ -17,7 +17,7 @@ QUARK_MXFP4_AVAILABLE = find_spec("quark") is not None and version.parse(
 ) >= version.parse("0.8.99")
 
 TRTLLM_GEN_MXFP4_AVAILABLE = (
-    current_platform.is_cuda() and current_platform.is_device_capability(100)
+    current_platform.is_cuda() and current_platform.is_device_capability_family(100)
 )
 
 HOPPER_MXFP4_BF16_AVAILABLE = (
@@ -799,7 +799,7 @@ def test_flashinfer_cutlass_mxfp4_fused_moe(
 @pytest.mark.skipif(
     not (
         current_platform.is_cuda()
-        and current_platform.is_device_capability(100)
+        and current_platform.is_device_capability_family(100)
         and has_flashinfer()
     ),
     reason="NVIDIA GPU sm100 and flashinfer are required for this test",

tests/quantization/test_blackwell_moe.py

@@ -10,9 +10,9 @@ import pytest
 from tests.utils import RemoteOpenAIServer
 from vllm.platforms import current_platform
 
-if not current_platform.is_device_capability(100):
+if not current_platform.is_device_capability_family(100):
     pytest.skip(
-        "This test only runs on Blackwell GPUs (SM100).", allow_module_level=True
+        "This test only runs on Blackwell GPUs (SM10x).", allow_module_level=True
     )
 
 

vllm/model_executor/layers/batch_invariant.py

@@ -936,7 +936,7 @@ def enable_batch_invariant_mode():
     # Batch invariant matmuls are no longer needed after cublas overrides
     if not is_torch_equal_or_newer("2.10.0.dev"):
         if (
-            current_platform.is_device_capability(100)
+            current_platform.is_device_capability_family(100)
             or current_platform.is_device_capability(80)
             or current_platform.is_device_capability(89)
         ):

vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py

@@ -287,7 +287,10 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         """
         DeepGemm supports packed ue8m0 activation scales format in devices == sm100
         """
-        return is_deep_gemm_e8m0_used() and current_platform.is_device_capability(100)
+        return (
+            is_deep_gemm_e8m0_used()
+            and current_platform.is_device_capability_family(100)
+        )
 
     def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
         # Let PrepareAndFinalize::finalize() decide the impl.

vllm/model_executor/layers/quantization/fp8.py

@@ -137,7 +137,7 @@ def get_fp8_moe_backend(
     if (
         current_platform.is_cuda()
         and (
-            current_platform.is_device_capability(100)
+            current_platform.is_device_capability_family(100)
             or current_platform.is_device_capability(90)
         )
         and envs.VLLM_USE_FLASHINFER_MOE_FP8
@@ -148,7 +148,7 @@ def get_fp8_moe_backend(
             logger.info_once("Using FlashInfer FP8 MoE TRTLLM backend for SM100")
             return Fp8MoeBackend.FLASHINFER_TRTLLM
         else:
-            if block_quant and current_platform.is_device_capability(100):
+            if block_quant and current_platform.is_device_capability_family(100):
                 raise ValueError(
                     "FlashInfer FP8 MoE throughput backend does not "
                     "support block quantization. Please use "
@@ -193,7 +193,7 @@ def get_fp8_moe_backend(
     # CUTLASS BlockScaled GroupedGemm on SM100 with block-quantized weights
     if (
         current_platform.is_cuda()
-        and current_platform.is_device_capability(100)
+        and current_platform.is_device_capability_family(100)
         and block_quant
     ):
         logger.info_once(

vllm/model_executor/layers/quantization/mxfp4.py

@@ -118,19 +118,19 @@ def get_mxfp4_backend(with_lora_support: bool) -> Mxfp4Backend:
             logger.info_once("Using FlashInfer MXFP4 BF16 backend for SM90")
             return Mxfp4Backend.SM90_FI_MXFP4_BF16
         elif (
-            current_platform.is_device_capability(100)
+            current_platform.is_device_capability_family(100)
             and has_flashinfer()
             and envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS
         ):
             logger.info_once("Using FlashInfer MXFP4 MXFP8 CUTLASS backend for SM100")
             return Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS
         elif (
-            current_platform.is_device_capability(100)
+            current_platform.is_device_capability_family(100)
             and has_flashinfer()
             and envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
         ):
             return Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
-        elif current_platform.is_device_capability(100) and has_flashinfer():
+        elif current_platform.is_device_capability_family(100) and has_flashinfer():
             logger.info_once(
                 "Using FlashInfer MXFP4 BF16 backend for SM100, "
                 "For faster performance on SM100, consider setting "
@@ -139,7 +139,7 @@ def get_mxfp4_backend(with_lora_support: bool) -> Mxfp4Backend:
             )
             return Mxfp4Backend.SM100_FI_MXFP4_BF16
         elif (
-            current_platform.is_device_capability(100)
+            current_platform.is_device_capability_family(100)
             or current_platform.is_device_capability(90)
         ) and not has_flashinfer():
             logger.warning_once(

vllm/model_executor/layers/quantization/utils/flashinfer_fp4_moe.py

@@ -50,7 +50,7 @@ def is_flashinfer_fp4_cutedsl_moe_available() -> bool:
         envs.VLLM_USE_FLASHINFER_MOE_FP4
         and has_flashinfer_cutedsl_grouped_gemm_nt_masked()
         and current_platform.is_cuda()
-        and current_platform.is_device_capability(100)
+        and current_platform.is_device_capability_family(100)
     )
 
 

vllm/model_executor/layers/quantization/utils/flashinfer_utils.py

@@ -290,7 +290,7 @@ def get_flashinfer_moe_backend() -> FlashinferMoeBackend:
     if flashinfer_moe_backend in backend_map:
         if (
             flashinfer_moe_backend == "latency"
-            and not current_platform.has_device_capability(100)
+            and not current_platform.is_device_capability_family(100)
         ):
             logger.info_once(
                 "Flashinfer TRTLLM MOE backend is only supported on "

vllm/model_executor/layers/quantization/utils/fp8_utils.py

@@ -247,7 +247,7 @@ class W8A8BlockFp8LinearOp:
         self.act_quant_group_shape = act_quant_group_shape
         self.is_deep_gemm_supported = is_deep_gemm_supported()
         self.is_hopper = current_platform.is_device_capability(90)
-        self.is_blackwell = current_platform.is_device_capability(100)
+        self.is_blackwell = current_platform.is_device_capability_family(100)
         self.use_deep_gemm_e8m0 = is_deep_gemm_e8m0_used()
 
         # Get the correct blockscale mul and input quant operations.

vllm/model_executor/layers/quantization/utils/mxfp4_utils.py

@@ -63,7 +63,7 @@ def _swizzle_mxfp4(quant_tensor, scale, num_warps):
                 "split_k": 1,
             }
             opt_flags.update_opt_flags_constraints(constraints)
-        elif current_platform.is_device_capability(100):
+        elif current_platform.is_device_capability_family(100):
             constraints = {
                 "is_persistent": True,
                 "epilogue_subtile": 1,

vllm/model_executor/models/config.py

@@ -363,7 +363,7 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
         else:
             kernel_block_alignment_size = 16
             if (
-                current_platform.is_device_capability(100)
+                current_platform.is_device_capability_family(100)
                 and model_config.get_head_size() == 256
                 and (
                     attention_config.backend is None

vllm/platforms/cuda.py

@@ -182,7 +182,7 @@ class CudaPlatformBase(Platform):
 
             if vllm_config.attention_config.backend is None:
                 # Default case
-                if cls.is_device_capability(100) and not use_sparse:
+                if cls.is_device_capability_family(100) and not use_sparse:
                     # Blackwell => Force CutlassMLA (unless sparse, i.e. DSv3.2).
                     use_cutlass_mla = True
                     # Set the backend in AttentionConfig so it's used during

vllm/platforms/interface.py

@@ -301,6 +301,21 @@ class Platform:
 
         return current_capability.to_int() == capability
 
+    @classmethod
+    def is_device_capability_family(
+        cls,
+        capability: int,
+        device_id: int = 0,
+    ) -> bool:
+        """
+        Returns True if the device capability is any <major>.x.
+        Mirrors CUDA 13 'family' architecture semantics (e.g. 10.x, 11.x, 12.x).
+        """
+        current_capability = cls.get_device_capability(device_id=device_id)
+        if current_capability is None:
+            return False
+        return (current_capability.to_int() // 10) == (capability // 10)
+
     @classmethod
     def get_device_name(cls, device_id: int = 0) -> str:
         """Get the name of a device."""

vllm/utils/deep_gemm.py

@@ -38,7 +38,7 @@ class DeepGemmQuantScaleFMT(Enum):
             return DeepGemmQuantScaleFMT.FLOAT32
         return (
             DeepGemmQuantScaleFMT.UE8M0
-            if current_platform.is_device_capability(100)
+            if current_platform.is_device_capability_family(100)
             else DeepGemmQuantScaleFMT.FLOAT32_CEIL_UE8M0
         )
 
@@ -50,7 +50,7 @@ def is_deep_gemm_supported() -> bool:
     """
     is_supported_arch = current_platform.is_cuda() and (
         current_platform.is_device_capability(90)
-        or current_platform.is_device_capability(100)
+        or current_platform.is_device_capability_family(100)
     )
     return envs.VLLM_USE_DEEP_GEMM and has_deep_gemm() and is_supported_arch
 

vllm/utils/flashinfer.py

@@ -264,7 +264,9 @@ def supports_trtllm_attention() -> bool:
         return False
 
     # Requires SM100 and NVIDIA artifactory to be accessible to download cubins
-    return current_platform.is_device_capability(100) and has_nvidia_artifactory()
+    return (
+        current_platform.is_device_capability_family(100) and has_nvidia_artifactory()
+    )
 
 
 def force_use_trtllm_attention() -> bool | None:

vllm/v1/attention/backends/flashinfer.py

@@ -564,7 +564,7 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
         )
         self.paged_kv_last_page_len_np = self.paged_kv_last_page_len_cpu.numpy()
 
-        if self.head_dim == 256 and current_platform.is_device_capability(100):
+        if self.head_dim == 256 and current_platform.is_device_capability_family(100):
             # https://github.com/flashinfer-ai/flashinfer/issues/1993 reports that
             # head size 256 and block size 16 is not supported on blackwell.
             assert kv_cache_spec.block_size != 16, (

vllm/v1/attention/backends/mla/common.py

@@ -446,7 +446,7 @@ def use_flashinfer_prefill() -> bool:
         and flashinfer_available
         and not vllm_config.attention_config.use_cudnn_prefill
         and not vllm_config.attention_config.use_trtllm_ragged_deepseek_prefill
-        and current_platform.is_device_capability(100)
+        and current_platform.is_device_capability_family(100)
     )
 
 
@@ -457,7 +457,7 @@ def use_cudnn_prefill() -> bool:
     return (
         flashinfer_available
         and vllm_config.attention_config.use_cudnn_prefill
-        and current_platform.is_device_capability(100)
+        and current_platform.is_device_capability_family(100)
         and has_nvidia_artifactory()
     )
 
@@ -470,7 +470,7 @@ def use_trtllm_ragged_deepseek_prefill() -> bool:
     return (
         flashinfer_available
         and vllm_config.attention_config.use_trtllm_ragged_deepseek_prefill
-        and current_platform.is_device_capability(100)
+        and current_platform.is_device_capability_family(100)
     )
 
 

vllm/v1/attention/backends/mla/flashmla_sparse.py

@@ -420,7 +420,7 @@ class FlashMLASparseMetadataBuilder(AttentionMetadataBuilder[FlashMLASparseMetad
         max_num_sm_parts = int(
             max((sm_count // 2) / h_k // (cdiv(h_q // h_k, 2 * 64) * s_q), 1)
         )
-        if current_platform.is_device_capability(100):
+        if current_platform.is_device_capability_family(100):
             max_num_sm_parts *= 2
         self.tile_scheduler_metadata_buffer = torch.empty(
             # TileSchedulerMetaDataSize = 8
@@ -719,7 +719,7 @@ class FlashMLASparseImpl(MLACommonBaseImpl[FlashMLASparseMetadata]):
         self.softmax_scale = scale
         assert indexer is not None
         self.topk_indices_buffer = indexer.topk_indices_buffer
-        self.padding = 128 if current_platform.is_device_capability(100) else 64
+        self.padding = 128 if current_platform.is_device_capability_family(100) else 64
 
         if kv_cache_dtype == "fp8_ds_mla":
             # Reserve workspace during initialization