VLLM Large Scale Serving: DeepSeek 2.2k Tok/S/H200 with Wide-EP (blog.vllm.ai)

vLLM has announced the successful migration to its new V1 engine architecture, achieving an impressive throughput of 2.2k tokens per second per H200 GPU in large-scale deployments. This milestone is the result of extensive work from a community of nearly 2,000 contributors and illustrates vLLM's standing as a leader in high-performance large language model (LLM) inference. Major companies like Meta, LinkedIn, and HuggingFace have already adopted vLLM, reflecting its growing industry importance. The new optimizations, including dual-batch overlap (DBO), expert parallel load balancing (EPLB), and wide-expert parallelism (Wide-EP), enhance efficiency for disaggregated serving models. By effectively managing sparse expert activation and utilizing advanced microbatching strategies, vLLM minimizes communication bottlenecks and load imbalances during inference. These enhancements not only promise immediate operational cost reductions but also set the stage for future developments in LLM serving and deployment, making it a significant step forward for the AI/ML community in harnessing the capabilities of large language models.