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Luce-Org/lucebox

LLM speculative inference server for heterogeneous hardware & consumer GPUs observed · 2026-08-28

github.com/Luce-Org/lucebox · homepage · C++ · Apache-2.0 (permissive) 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

Full methodology

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

Member repositories

RepositoryRoleHealth v2
Luce-Org/luceboxmain59

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