Unlocking asynchronicity in continuous batching

Unlocking asynchronicity in continuous batching Unlocking asynchronicity in continuous batching Published May 14, 2026 Update on GitHub Upvote 67 Rémi Ouazan Reboul ror Pedro Cuenca pcuenq Aritra Roy Gosthipaty ariG23498 TL;DR: we explain how to separate CPU and GPU workloads to get a massive performance boost for inference. This is the second post in a series on efficient LLM inference.

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• Unlocking asynchronicity in continuous batching Published May 14, 2026 Update on GitHub Upvote 67 Rémi Ouazan Reboul ror Pedro Cuenca pcuenq Aritra Roy Gosthipaty ariG23498 TL;DR: we explain how to separate CPU and GPU workloads to get a massive performance boost for inference.
• This is the second post in a series on efficient LLM inference.
• The first post covered continuous batching from first principles.
• It introduces some concepts we build upon: KV cache, FlashAttention, attention masks, etc.
• An H200 costs around $5 an hour on Inference Endpoints .
• That's cheap for an hour, but use it for a day and you are already paying $120.