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.
More from Infra
- AI chip startup Tenstorrent raises $1.5B at $6.2B valuation, more than doubling — Beth_Kindig · 2026-10-27
- Anthropic discloses up to $120.5B SpaceX compute deal, ~$3.1B monthly at full capacity — XFreeze · 2026-10-27
- Satire: swapping Raft for an LLM-style decision model tanks write throughput 99.98% — irombie · 2026-10-27
- Cadence joins the domain-specific AI model camp for chip design — karlfreund · 2026-10-27
- Qdrant builds Universal I/O to run vector search mmap code on object storage — qdrant_engine · 2026-10-27
- Nebius secures $3.5B Citi financing facility for GPU purchases and AI data centers — Beth_Kindig · 2026-10-26