Luce-Org/lucebox
LLM speculative inference server for heterogeneous hardware & consumer GPUs observed · 2026-08-28
Health v2 · maintenance only
59/100
- Activity 99
- Release rhythm 35
- Longevity 10
Flags: no_releases young
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 152
- days_rel: n/a
- days_push: 7
- n_releases_24m: 0
Adoption not part of the score
2788 stars · 265 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Lucebox is an open-source LLM inference engine written in C++ with custom CUDA/HIP kernels, focused on speculative decoding and speculative prefill for heterogeneous hardware and consumer GPUs. It ships as a server with OpenAI- and Anthropic-compatible APIs and is tuned per model and hardware target (e.g., RTX 3090, Strix Halo, AMD R9700).
Use cases
- run large language models locally on consumer GPUs
- speed up long-context prefill on a single GPU
- serve LLMs with an OpenAI-compatible API on self-hosted hardware
- combine unified memory and dedicated GPU for large-model inference
- get faster token generation with speculative decoding
- run inference across mixed CPU/GPU heterogeneous machines
- keep AI inference fully private on owned hardware
When to choose
- you want maximum tokens/sec on consumer GPUs like RTX 3090 or AMD R9700
- you need fast time-to-first-token on very long prompts (64K-256K tokens)
- you want to mix unified-memory APUs with discrete GPUs in one inference setup
- you prefer a C++/CUDA engine with no Python, PyTorch, or Triton in the inference loop
- you want a plug-and-play private local AI server with compatible APIs
When to avoid
- you need broad multi-vendor model support beyond the tuned model list
- you require multi-node distributed inference at datacenter scale
- you need a Python ecosystem for custom model experimentation
- you only need CPU-only inference without GPU acceleration
- you want a mature, widely-deployed engine like vLLM with extensive community tooling
Facets
service · maturity active
llm-inference gpu-computing http-server sdk large-language-models machine-learning gpu-computing self-hosted developer-tools self-hosted cpp speculative-decoding speculative-prefill cuda-kernels rocm heterogeneous-computing consumer-gpus local-ai openai-compatible-api llama-cpp moe-inference paged-attention long-context linux gpu cuda
3 sources
- readme: https://github.com/Luce-Org/lucebox · fetched 2026-08-28 · 0aa143a8e864
- homepage: https://www.lucebox.com · fetched 2026-08-29 · 23754db01391
- site_page: https://www.lucebox.com/blog/pflash · fetched 2026-08-29 · ff894ee9f923
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Luce-Org/lucebox | main | 59 |
For agents
markdown · JSON · MCP: product_card(name="Luce-Org/lucebox")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem