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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 8, 17:45 UTC

[Kernel] Add enable_sm120_or_later for SM121 (DGX Spark) CUTLASS support (#33517)

Signed-off-by: code4me2 <velvetmoon222999@gmail.com>

Vel committed Feb 6, 2026 at 20:28 UTC bc32444b238d2ec3726f599cf3fc67dbaf51a6c6
2 files changed +13 -1
csrc/cutlass_extensions/common.hpp
+11
@@ -152,3 +152,14 @@ struct enable_sm120_only : Kernel {
152 #endif
153 }
154 };
155 +
156 +// SM12x family includes SM120 (RTX 5090) and SM121 (DGX Spark GB10)
157 +template <typename Kernel>
158 +struct enable_sm120_family : Kernel {
159 + template <typename... Args>
160 + CUTLASS_DEVICE void operator()(Args&&... args) {
161 +#if defined __CUDA_ARCH__ && (__CUDA_ARCH__ >= 1200 && __CUDA_ARCH__ < 1300)
162 + Kernel::operator()(std::forward<Args>(args)...);
163 +#endif
164 + }
165 +};
csrc/quantization/w8a8/cutlass/c3x/scaled_mm_blockwise_sm120_fp8_dispatch.cuh
+2 -1
@@ -103,7 +103,8 @@ struct cutlass_3x_gemm_fp8_blockwise {
103 MainloopScheduler
104 >::CollectiveOp;
105
106 - using KernelType = enable_sm120_only<cutlass::gemm::kernel::GemmUniversal<
106 + // SM12x family to support both SM120 (RTX 5090) and SM121 (DGX Spark)
107 + using KernelType = enable_sm120_family<cutlass::gemm::kernel::GemmUniversal<
108 Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue>>;
109
110 struct GemmKernel : public KernelType {};