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Reame — CPU-first LLM inference server on llama.cpp: disk KV cache, self-regulating speculation, generation archive, interleaved multi-user, the Conclave. Your hardware, your realm.
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.
| 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.
--best-of N generates N
candidate answers to the same prompt in one interleaved batch (one prefill,
cloned into the others via KV copy; every weight read shared) and elects the
winner by majority on the final result. The moment an absolute majority
agrees, the stragglers are stopped. Honestly measured: it squeezes roughly
one extra correct answer per quiz out of the model you already run — it
does not make a 1.5B out-reason a 3B (consensus fixes variance, not bias)./v1/completions, /v1/chat/completions,
SSE streaming, sessions, bearer auth, metrics. Point any OpenAI client at it.reame run qwen2.5-1.5b downloads the model once,
autoconfigures threads/KV/cache for the host and drops into a chat (or
--serve). No config file until you want one.Every number below was produced by the shipped binary on the hardware named — including the negative results that shaped the design.
| Hardware | Model | Configuration | Result |
|---|---|---|---|
| Oracle Cloud free tier (2× ARM, 12 GB, €0/mo) | Qwen2.5-7B Q4_K_M | plain, KV q8_0 | 3.3 tok/s |
| Oracle Cloud free tier | TriLM 3.9B ternary TQ2_0 | 1.1 GB total RAM | ~10 tok/s |
| Apple M3 Pro (6 threads) | Qwen2.5-1.5B Q4_K_M | plain | 52 tok/s |
| Shared Contabo VPS (18 oversubscribed vCPUs) | 1.5B + 0.5B draft | speculative, 87% acceptance | 3.2× speedup |
| Shared Contabo VPS | TinyLlama 1.1B | warm disk cache vs cold | 4.8× end-to-end |
| Apple M3 Pro | Qwen2.5-1.5B | prompt-lookup on a rewrite task | 1.44× |
| Apple M3 Pro | TinyLlama, 3 concurrent users | interleaved vs serialized | 1.6× |
| Apple M3 Pro | Qwen2.5-1.5B, repeated request | archive speculation (palimpsest) | 2.3× (22→51 tok/s) |
| Apple M3 Pro | Qwen2.5-1.5B, fresh list generation | form drafting (suggeritore) | 2.1× (4.4s→2.1s) |
| Apple M3 Pro | Qwen2.5-1.5B ×5 candidates | Conclave: shared prefill + early consensus + fast nucleus | 8-question quiz wall 97s → ~50s |
| Apple M3 Pro | Qwen2.5-1.5B --best-of 5 vs single |
3 arithmetic quizzes, strict grading | +0.5 to +2 correct, ~2.5× wall (not 5×) |
| Oracle Cloud free tier | OLMoE 7B-A1B (MoE) vs dense 7B | same 8-needle long-context test | 100% accuracy both · 17.8 vs 3.3 tok/s (5.4×) |
Two negative results that matter. On heavily oversubscribed shared vCPUs a draft model runs as slowly as its target, so speculation is counter-productive there — Reame detects this and disables it at runtime. And the Conclave does not close the gap to a model twice the size on hard reasoning: majority voting corrects random slips, not systematic misunderstanding — we measured a 1.5B ×5 land between the 1.5B and a 3B, never above the 3B. Benchmarks that only show wins are advertising; these are engineering.
Shared-prefix disk cache. Prompts are split into fixed token blocks; a chain hash keys a KV snapshot at every block boundary. A different prompt that shares a prefix restores the longest cached boundary and decodes only its own tail. Unlike GPU-resident prefix caches, snapshots live on NVMe: they survive restarts.
Self-regulating speculation. Classic Leviathan/Chen acceptance (the rejected token is resampled from the residual distribution, so the output distribution is exactly the target's), with two CPU-first twists: the draft source can be free n-gram lookup mined from the prompt itself — ideal for extraction and rewrite workloads — and a feedback controller adapts the draft length and turns speculation off when measured acceptance or draft economics go negative.
The Conclave. --best-of N submits N attempts at the same prompt to the
interleaved scheduler: attempt 0 is the untouched anchor (greedy stays greedy),
the explorers shift seed and heat up. The scheduler notices the identical
prompts and clones the prompt KV instead of prefilling N times (copy the
donor's cache, decode only the last prompt token — argmax-verified equal to a
full prefill). Election is an exact-majority vote on each candidate's final
number, with a Jaccard text-medoid fallback for prose; the moment a majority
exists the remaining candidates are stopped mid-generation, and the CLI reports
CONCLAVE consensus=k/N so a caller can escalate only when the conclave split.
Use it as a quality knob: more accuracy from the model your hardware can
afford, paid in idle interleaved compute rather than a bigger model's RAM.
reame list # model catalog + what's on disk reame run qwen2.5-1.5b # download once, auto-config, chat reame run qwen2.5-1.5b "Explain mmap" # one-shot answer reame run qwen2.5-1.5b --serve # OpenAI-compatible API on :8080 reame run qwen2.5-1.5b "12*13-50?" --best-of 5 # the Conclave
run resolves a catalog name (or any local GGUF path), downloads to
~/.reame/models on first use and picks threads, KV quantization and cache
directory for the host. A config file is only needed when you want control.
Homebrew (macOS / Linux):
brew tap swellweb/reame brew install reame
Prebuilt binaries — Linux x64/arm64 and macOS arm64 on the releases page (runtime dependency: libzstd).
npm (npx reame): planned — binaries are already built per platform.
git clone https://github.com/swellweb/reame cd reame git submodule update --init --depth 1 third_party/llama.cpp ./build.sh # Release build + 210 test cases ./scripts/download_models.sh # TinyLlama (test model, ~670 MB) ./build/src/reame --config config/reame.conf --prompt "Hello" --max-tokens 32 ./build/src/reame --config config/reame.conf --serve # OpenAI-compatible API
Dependencies: CMake ≥ 3.16, a C++17 compiler, and for the server Boost (headers), nlohmann-json and zstd:
# Debian/Ubuntu sudo apt install build-essential cmake libboost-dev nlohmann-json3-dev libzstd-dev pkg-config # macOS brew install cmake boost nlohmann-json zstd pkg-config
[model] path = models/qwen2.5-7b-instruct-q4_k_m.gguf context_length = 4096 # total KV budget (shared across users when parallel > 1) threads = 4 # fewer is often faster on shared vCPUs — measure! [memory] kv_cache_type = q8_0 # f16 | q8_0 | q4_0 — halve/quarter context RAM [speculative] enabled = true mode = lookup # model (needs draft_model_path) | lookup (no 2nd model) [cache] directory = /opt/reame/cache max_size_mb = 4096 # LRU byte budget on disk [server] port = 8080 api_key = # bearer auth when set parallel = 1 # >1 = interleaved multi-user serving
| Endpoint | Description |
|---|---|
POST /v1/completions |
text completion (SSE with "stream": true) |
POST /v1/chat/completions |
chat completion |
POST /v1/sessions · .../save · .../load · DELETE .../{id} |
KV session snapshots |
GET /metrics |
request counters + speculative/cache metrics |
GET /health |
liveness (auth-exempt) |
Reame's footprint is watt-scale, not kilowatt-scale: it targets machines that already exist and are already powered on — no new silicon is racked to serve your model. We don't claim better joules-per-token than a saturated datacenter GPU — we claim you don't need one.
Reame is young and deliberately opinionated and focused: CPU-only serving, one model per process, correctness pinned by tests at every layer. Not goals: GPU offload, training, model management UX. The llama.cpp submodule is pinned to a known-good commit and bumped deliberately.
Documentation in Italian: docs/README.it.md.
The laptop story is the same one command: reame run qwen2.5-1.5b downloads,
autoconfigures and chats — nothing to learn. From there the two projects
diverge: Ollama optimizes for running many models casually; Reame optimizes
for serving one workload seriously on hardware that costs nothing. The
difference is one sentence:
Ollama runs models. Reame remembers having run them.
General-purpose servers treat every request as brand new: compute, discard, repeat. On a GPU that's fine — compute is cheap. On a cheap CPU, compute is the most expensive thing you have, and throwing it away is the cardinal sin. Everything in Reame attacks that: the disk prefix cache, the generation archive, the grammar prompter, self-regulating speculation, interleaved multi-user batches, the Conclave. None of it exists in Ollama.
The practical consequence: a Reame server gets faster the longer it runs. The hundredth request costs a fraction of the first — the system prompt was paid once, similar answers draft themselves from the archive, structure is speculated for free. No other server has that property.
Reame is free, MIT-licensed and built on nights and free-tier hardware. If it saves you API bills or GPU rent, consider sponsoring the work — sponsorships fund the roadmap: ARCA (the shared memory daemon), warm-ahead prefill, and first-class MoE serving.
