GoKawiilA 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.
What Reame is for
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.
Use case Why it fits Suggested model Document extraction & classification (RAG, invoices, tickets, scraping) answers live in the context; prompts share prefixes → the disk cache pays Qwen2.5 1.5B–7B Batch pipelines (tag 10k products overnight, meta descriptions, email triage) repetitive by nature → Palimpsest drafts them; €0 per token, no rate limits Qwen2.5 1.5B–3B AI features inside a thin-margin SaaS a €5 VPS instead of a metered API keeps unit economics alive Qwen2.5 1.5B–7B Privacy-bound work (legal, medical, public sector) data never leaves your server — full sovereignty Qwen2.5 7B Private code autocomplete (Continue.dev + OpenAI-compatible API) line-level completion is a narrow task; code never leaves the laptop Qwen2.5-Coder 1.5B
What Reame is NOT for — said plainly, because trust is built here: a general-purpose ChatGPT replacement (frontier reasoning and broad knowledge need frontier parameter counts), agentic coding assistants, or creative long-form writing at scale. If your task needs a 100B-class brain, buy one; if it needs your documents processed privately, forever, at zero marginal cost — that's a realm you can own.
🗂️ Persistent shared-prefix KV cache — prompt prefixes are snapshotted to disk (zstd, checksummed, LRU-budgeted) and reused across different prompts, restarts and processes . A system prompt is paid for once, by the first user.
— prompt prefixes are snapshotted to disk (zstd, checksummed, LRU-budgeted) and reused . A system prompt is paid for once, by the first user. 📜 Palimpsest: the server remembers what it generated — every completed generation feeds an on-disk n-gram archive; future requests draft from it at zero cost. Domain workloads repeat themselves — let them pay off.
— every completed generation feeds an on-disk n-gram archive; future requests draft from it at zero cost. Domain workloads repeat themselves — let them pay off. 🎭 Il Suggeritore: grammar as a draft source — constrained decoding uses structure to forbid tokens; Reame inverts it and uses structure to propose them. List numbering, bullets and format tokens are speculated for free on content nobody has ever generated before.
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