The Storage Technology Behind Context Caching
The context caching offered by major LLM APIs is essentially persistent KV cache reuse: cache-hit input is billed at a discount. A self-hosted inference platform gets the same mechanism with external KV tiering — Mingxin measured throughput +29–40%.
API vendors have already proven the business value
Mainstream LLM APIs (DeepSeek, Kimi, Qwen, OpenAI and others) all offer context-cache pricing: cache-hit input tokens cost significantly less than full price — directly reflecting the real compute saved by KV reuse (see each vendor's public price page).
For a self-hosted platform this cost structure does not appear automatically: open-source vLLM's prefix caching covers only the GPU-memory tier, and sessions get evicted as soon as volume grows. To reach API-vendor-grade cache economics, the KV tier must be persisted to external storage.
The implementation path for self-hosted platforms
vLLM + LMCache + external all-flash is the working trio. Mingxin provides source-level ROCm adaptation, the parallel-read patch (R1, TTFT 4.1×) and production sizing; the 480B production form measured throughput +29–40% and 8.6–20× vs re-compute (R2/R3).
The key differentiator is cross-instance sharing: the fs:// shared pool lets any instance in the cluster hit the same cache (verified in R3) — something single-machine setups structurally cannot do.
FAQ
Which applications benefit most from context caching?
Long system prompts, document Q&A, multi-turn chat, agent workflows — any workload with heavily repeated input prefixes. One-shot short Q&A benefits little.
How is cache security handled?
KV blocks are tenant-isolated and prefix-hash addressed; the external tier is your own privately deployed asset, bypassing third-party APIs, with a cleaner data boundary than public-cloud caching.
Is there a ready-made benefit calculator?
The on-site ROI calculator is built on the reproducible mingxin/accel_value.py model — enter GPU count and cluster size to get a benefit range with sensitivity analysis.
Data sources (verifiable)
Related reading
- KV Cache Offloading: Putting the Inference Cache on an All-Flash Array
- Long-Context Cold Recovery: The Main Performance Battleground of the Agent Era
- KV Cache Tiering: The Three-Level HBM → RAM → All-Flash Architecture
Joint test first, decisions second: gate-based acceptance with built-in stop-loss
The full costing model is provided as reproducible Python after NDA — customers can rerun it with their own parameters. Every key figure on this site carries a report ID and is open to third-party verification.
This site presents business-cooperation information and constitutes neither an investment offer nor any promise of returns. Measured data come from signed / official test reports (see the Evidence Library); vendor specs, public sources and estimates are labeled as such.