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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 5, 04:45 UTC

[Bugfix] Disable FlashInfer CUTLASS MoE on SM121 (DGX Spark) (#39825)

Signed-off-by: mgoin <mgoin64@gmail.com> Co-authored-by: Claude <noreply@anthropic.com>

Michael Goin committed Apr 14, 2026 at 19:03 UTC 65b9808960d7f25a018c525d2aca3dba7c411cfb
1 file changed +8 -1
vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py
+8 -1
@@ -130,7 +130,14 @@ class FlashInferExperts(mk.FusedMoEExpertsModular):
130 p.is_device_capability(90)
131 or p.is_device_capability_family(100)
132 or p.is_device_capability_family(110)
133 - or p.is_device_capability_family(120)
133 + or p.is_device_capability(120)
134 + # NOTE: SM121 (DGX Spark) is excluded because the bf16
135 + # unquantized CUTLASS MoE GEMM in flashinfer <= 0.6.7 has no
136 + # Relu2 template instantiation and throws "Invalid activation
137 + # type" on Nemotron-H. Fixed upstream by
138 + # https://github.com/flashinfer-ai/flashinfer/pull/2926
139 + # (merged 2026-04-01, not yet in a stable release); lift this
140 + # restriction once flashinfer >= 0.6.8 is the minimum.
141 )
142 and has_flashinfer_cutlass_fused_moe()
143 )