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vLLM
@vllm_project
A high-throughput and memory-efficient inference and serving engine for LLMs. Join to discuss together with the community!
Joined March 2024
37 Following    54.1K Followers
The hardware work reaches across the serving stack. NVIDIA's FlashInfer and vLLM optimizations include fused norm/gating/residual kernels, TEP pooler/unpooler sharding with fused MNNVL collectives, and an FP32-norm-to-TF32-router path. AMD MI355X supports FP8 and MXFP4 with TP/PP on ROCm. Initial TPU support uses TorchTPU. With DSpark (acceptance length = 6), Muse Spark Open megakernels reach over 650 tokens/sec on 4 TPU v7 chips and 960 tokens/sec on 8 chips; without speculation, over 270 and 350 tokens/sec. Code access is available through the team or Google. 4/6
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