Show HN: MemStitch – Zero-copy context bridging for vLLM (25x TTFT speedup)
MemStitch is a new tool designed to bridge memory caches between agents in multi-agent GPU inference workflows. It achieves a 25x speedup in Time-to-First-Token (TTFT) by eliminating redundant prefill phases.
Why it matters
This optimization significantly reduces latency and VRAM usage for complex AI pipelines, making multi-agent systems more efficient and scalable.
Zero-Copy Context Bridging Gateway for Multi-Agent GPU Inference.
In multi-agent collaborative workflows, separate agents often process the same long text context sequentially. For example:
Under standard inference engines, Agent B is forced to repeat the expensive prefill phase , duplicate GPU activations, and suffer from high Time-to-First-Token (TTFT) latency.
Context-Stitcher solves this by bridging caches at the memory level:
Below is the benchmark analysis of Context-Stitcher compared to standard vLLM cold-prefills when executing consecutive agents over a shared 200-page document:
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