ContextCache: 29x TTFT Speedup for Tool LLMs
π‘29x tool-calling speedup via KV cache, open-source code β transform your LLM inference
β‘ 30-Second TL;DR
What Changed
Caches KV states via SHA256 hash of sorted schema texts
Why It Matters
Enables scalable multi-tool LLM deployments with near-zero prefill latency, critical for production tool-augmented agents. Democratizes fast inference via open-source, potentially standardizing KV caching practices.
What To Do Next
Clone https://github.com/spranab/contextcache and test on your Qwen tool-calling setup.
Key Points
- β’Caches KV states via SHA256 hash of sorted schema texts
- β’Group caching preserves cross-tool attention for full accuracy
- β’29x speedup on Qwen3-8B: 200ms TTFT vs 5.6s full prefill at 50 tools
- β’99% prompt tokens skipped with matching benchmarks (TSA, PF1, EM)
- β’GitHub code released for integration
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Original source: Reddit r/MachineLearning β
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