ray-serve

Tag

Cards List
#ray-serve

@raydistributed: Ray Serve LLM now offers 4.4x higher request throughput on prefill-heavy workloads, and 24.8x higher request throughput…

X AI KOLs Following · yesterday Cached

Ray Serve LLM achieves 4.4x and 24.8x throughput improvements on prefill- and decode-heavy workloads via direct streaming, a new vLLM V2 executor backend, and HAProxy ingress, now available in Ray 2.56 in partnership with Google Cloud and vLLM.

0 favorites 0 likes
#ray-serve

@anyscalecompute: Most agent frameworks solve orchestration and leave infrastructure completely unresolved. New blog: production-ready AI…

X AI KOLs Following · 2026-05-07 Cached

Anyscale published a technical guide on deploying production-ready AI agents using Ray Serve, MCP, and A2A protocols. The article addresses common infrastructure bottlenecks by proposing a decoupled microservices architecture that enables independent scaling of LLMs, tools, and agents.

0 favorites 0 likes
← Back to home

Submit Feedback