AI BriefingHelion Linear Backend for vLLM Delivers >10% Throughput Gains on Some Hopper Workloads
·2026.10.20 21:52
Key point
A new Helion linear backend for vLLM uses AOT autotuning to outperform CUTLASS and DeepGEMM baselines on NVIDIA Hopper GPUs, with >10% throughput gains on some workloads.
Details
- Helion linear backend integrated into vLLM achieves consistent end-to-end performance gains, with throughput improvements of over 10% on some workloads compared to default backends.
- Kernel-level benchmarks on NVIDIA H100 GPUs show geometric mean speedups of 1.110x over CUTLASS for FP8_Dynamic, 1.178x over CUTLASS for W8A8_INT8, and 1.149x over FlashInfer for Block_FP8.
- The backend unifies Standard GEMM, Split-K, and Swap-AB algorithms into a single implementation, allowing an AOT autotuner to select optimal configurations per input shape.
- Hybrid dispatch strategy routes small token counts (num_tokens <= 32) to Helion under CUDA Graphs to eliminate CPU overhead, while falling back to CUTLASS/DeepGEMM for larger batches.
- Current availability is via a vLLM fork (github.com/redhat-et/vllm-helion) with pre-tuned configurations; upstream integration strategy is under review, potentially delegating workload-specific autotuning to end users.
- Trade-offs include significant AOT tuning overhead (hours) and cold-start latency due to JIT compilation, mitigated by caching and CUDA Graph execution.
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