Cuts Long Horizon Inference Costs by 50% via external KV Cache Offload
Hey HN, we’re the developers of OpenLake, an open source storage engine for offloading LLM KV caches from GPU memory into a shared tier of RAM and NVMe. We built OpenLake because KV caches are outgrowing GPU memory. A single 256K token conversation on Gemma 4 31B produces approximately 43GB of KV state, more than half the memory of an 80GB H100. The problem becomes even harder across a cluster: a prefix cached on one GPU host is unavailable when the next request lands on a different GPU, forcing the new GPU to repeat work the fleet has already completed. Once the KV cache is offloaded,…
Cuts Long Horizon Inference Costs by 50% via external KV Cache Offload launched on July 26, 2026 with 22 votes, #830 of 24,623 launches that month and more than 90% of that year's launches.
In plain words
OpenLake is an open source storage engine that offloads large language model KV caches from GPU memory to shared RAM and NVMe storage. It addresses the problem of KV caches exceeding GPU capacity during long conversations and across distributed clusters. The tool includes a custom CUDA kernel for lossless compression to reduce network overhead when retrieving cached data. It is designed for developers and organizations running LLM inference at scale who need to reduce memory constraints and improve efficiency.
written from the facts on this page · September 2026
From the sources
OpenLake is a high performance storage engine for efficient LLM inference and GPU Training - openlake-project/openlake
In the maker’s words, at launch
Hey HN, we’re the developers of OpenLake, an open source storage engine for offloading LLM KV caches from GPU memory into a shared tier of RAM and NVMe. We built OpenLake because KV caches are outgrowing GPU memory. A single 256K token conversation on Gemma 4 31B produces approximately 43GB of KV state, more than…
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