Show HN: Reame – a CPU inference server that gets faster as it runs
Reame is a new LLM inference server designed to optimize performance on low-cost CPU hardware. It uses a caching strategy to avoid redundant computations, making it suitable for private, repetitive data processing tasks.
Why it matters
It offers a cost-effective alternative for running AI models on existing hardware, potentially democratizing access to local LLM inference.
A lean, fully-tested LLM inference server built on llama.cpp — designed for the hardware you already have: shared vCPUs, free tiers, 2-core ARM boxes.
Reame is not the first inference server. It's the first one that treats cheap CPU hardware as a first-class citizen instead of a fallback. Its thesis is simple:
On a CPU, never compute the same thing twice.
Reame is built for narrow, repetitive AI workloads over your own data, on hardware you already pay for — the case where the answer lives in the context you provide, not in the model's general knowledge. That is exactly where a small model matches a frontier one (we measured 100% accuracy on long-context extraction with a 7B on a free 2-core ARM box) and where Reame's memory makes request #100 cost a fraction of request #1.
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