MemStitch – Zero-copy context bridging for vLLM (25x TTFT speedup)
Zero-Copy Context Bridging Gateway for Multi-Agent GPU Inference. Bypasses the expensive prefill phase by dynamically stitching KV Caches at the memory level using PagedAttention. Cuts TTFT latency by up to 25x and saves 40%+ VRAM for collaborative LLM workflows. - DaqulaLin/MemStitch
MemStitch – Zero-copy context bridging for vLLM (25x TTFT speedup) launched on July 14, 2026 with 12 votes, #1,321 of 24,623 launches that month and more than 90% of that year's launches.
In plain words
MemStitch is a tool for vLLM that enables zero-copy context bridging to speed up time-to-first-token (TTFT) performance. It is designed for developers working with large language models who need faster response times. The software eliminates data copying overhead when transferring context between components, achieving claimed speedups of up to 25 times for initial token generation. MemStitch is available as open-source code on GitHub.
written from the facts on this page · September 2026
From the sources
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. ⚡ TTFT Prefill Latency (Agent B Response Time) — Lower is better Baseline (vLLM Cold): ██████████████████████████████ 1200 ms Context-Stitcher: █ 48 ms ( 25.0x Prefill Speedup! 🚀 ) 💾 GPU Physical Cache Blocks Allocated (Total VRAM) — Lower is better Baseline (vLLM Cold): ██████████████████████████████ 53 blocks (No sharing)…from github.com
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