Back to directory

CrossPool

A serving system that lets multiple SGLang model instances share GPU memory by separating KV-cache capacity from FFN weight execution, so each model's resource needs no longer have to be reserved together.

CrossPool

CrossPool co-locates multiple SGLang models on shared GPUs when a model's KV-cache demand and its FFN weight requirements do not scale together. A conventional co-located deployment ties both resources to the same process and GPU reservation, forcing every instance to reserve both even when one mainly needs KV capacity and another mainly holds resident weights.

A daemon control plane registers participants, coordinates SLO-aware KV capacity, and tracks liveness. Each SGLang instance keeps logical KV and prefix-cache ownership while sending FFN work to shared agents that hold model-specific weight shards as reusable graph templates replayed through per-lane CUDA graphs; physical KV backing can move between co-located instances without sharing logical KV contents.

It installs as a pinned SGLang plugin that adds architecture-specific adapters without replacing SGLang's own request or KV-cache runtime, and is written in Python.

More in Inference

See all 770
  • OpenDecision

    deepanwadhwa.github.io

    An open-source Python package that answers typed questions about application state or documents using a local natural-language-inference model, returning structured choice, score, yes/no or evidence-relation values instead of free text.

  • Lifeboat

    iterate.ai

    A local inference server that puts an OpenAI- and Anthropic-compatible API in front of open-weight language models running on hardware you control, from a laptop to a GPU cluster.

  • Claude S40

    github.com

    An unofficial Claude chat client for 2007-era Nokia Series 40 phones, built as a Java ME app plus a small Go relay server, with web search, reading mode and saved chats.