Slashing LLM Inference Costs with KV Cache Offloading
algo_diver · x · 2026-07-12
This repost outlines practical strategies for driving LLM inference costs down even further:
- Upon receiving a request, it first checks LMCache; on a hit, it fetches the KV cache directly from CPU/SSD/remote storage, skipping the prefill phase.
- The combined stack includes LMCache + vLLM + CPU/SSD/remote storage + zero-copy CUDA kernels.
- The post notes that production systems like Bloomberg generate hundreds of terabytes of KV cache traffic weekly, proving such optimizations are already deployed in real-world environments.
The repost also references another article on Loop Engineering: automatically tuning RAG by building a closed-loop system that searches for configurations, tests regression rates on evaluations, and stops once targets are met—replacing manual hyperparameter tuning.
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