Speculative inference for heterogeneous machines and consumer GPUs.
Custom kernels, speculative prefill and decoding, tuned for each model and hardware target.

---

Inference Engine Optimizations

---

Supported Models and Drafters

Model links open the exact weights used by the measured setup. Drafter links open the published quant, or the source checkpoint when conversion is required.

Tested Machines (GPU/APU)

The engine is not tied to one reference card. NVIDIA architectures are selected by CMake; HIP builds should target the device's exact gfx architecture.

Single-device results

Heterogeneous and parallel results

These runs use different prompts, quantizations, and inference policies. They show which configurations work; they are not a cross-hardware ranking.

Recommended Setups

See Recommended server setups for the model and hardware matrix, including single-GPU and mixed-GPU profiles.

Client Harnesses

harness/ runs Lucebox through popular coding clients and checks server compatibility.

Set the server binary and model paths, then run a launcher:

DFLASH_SERVER_BIN=server/build/dflash_server \
DFLASH_TARGET=server/models/Qwen3.8-27B-UD-IQ4_XS.gguf \
DFLASH_DRAFT=server/models/draft/qwen38-dflash2-q8_0.gguf \
MAX_CTX=32768 \
harness/clients/run_codex.sh

See the harness guide for setup, no-draft targets, and benchmarks.

Quick Start With Docker

Prebuilt images on GHCR track main. Mount the weights and serve the OpenAI-compatible API on :8000.

Run the image for your GPU:

# NVIDIA
docker run --rm --gpus all -p 8000:8080 \
  -v "$PWD/server/models:/opt/lucebox-hub/server/models" \
  ghcr.io/luce-org/lucebox-hub:cuda12

# AMD
docker run --rm --device /dev/kfd --device /dev/dri \
  --group-add video --group-add render --security-opt seccomp=unconfined \
  -p 8000:8080 -v "$PWD/server/models:/opt/lucebox-hub/server/models" \
  ghcr.io/luce-org/lucebox-hub:rocm

Run the Server

This quick start runs the R9700 profile above. The complete flag reference is in the server guide.

# build (ROCm 7.2+, RDNA4)
git clone --recurse-submodules https://github.com/Luce-Org/lucebox.git
cd lucebox
cmake -S server -B server/build-hip -G Ninja \
  -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_HIP_COMPILER=/opt/rocm/lib/llvm/bin/clang++ \
  -DDFLASH27B_GPU_BACKEND=hip \
  -DDFLASH27B_HIP_ARCHITECTURES=gfx1201 \
  -DGGML_HIP_MMQ_MFMA=ON \
  -DGGML_HIP_NO_VMM=ON
cmake --build server/build-hip --target dflash_server -j"$(nproc)"

# target and DFlash2 drafter
mkdir -p models
huggingface-cli download unsloth/Qwen3.8-27B-GGUF \
  Qwen3.8-27B-UD-IQ4_XS.gguf --local-dir models
huggingface-cli download incoai/Qwen3.8-27B-DFlash2 --local-dir models/dflash2
python server/scripts/convert_dflash_to_gguf.py \
  models/dflash2/model.safetensors models/qwen38-dflash2-f16.gguf
python server/scripts/quantize_dflash_draft.py \
  models/qwen38-dflash2-f16.gguf models/qwen38-dflash2-q8_0.gguf --scheme q8_0

# launch the measured profile
./server/build-hip/dflash_server models/Qwen3.8-27B-UD-IQ4_XS.gguf \
  --draft models/qwen38-dflash2-q8_0.gguf \
  --draft-block-size 16 --max-ctx 131072 \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  --port 8216

curl -s http://127.0.0.1:8216/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"messages":[{"role":"user","content":"Write a Python LRU cache."}],
       "max_tokens":256,"temperature":0}'

Documentation

Benchmarks stay with each implementation: DFlash, PFlash, Spark, KVFlash, and Megakernel.

---

Tutorials

Video tutorials for each optimization and the harness setup.

---

The Lucebox Machine

Local AI should be the default, not a privilege. Private data, no per-token bill, no vendor lock-in. Lucebox pairs the R9700 with Strix Halo and ships this open engine ready to run.

See the hardware and current benchmarks at lucebox.com.

---

Request for Contributions

We welcome focused contributions to CUDA and HIP kernels, speculative inference, support for more consumer GPUs and APUs, performance benchmarks, and client harnesses.

---

Citation

@software{lucebox_2026,
  title  = {Lucebox: Speculative inference for heterogeneous consumer hardware},
  author = {Lucebox},
  url    = {https://github.com/Luce-Org/lucebox},
  year   = {2026}
}

---

Community

• Discord: discord.gg/yHfswqZmJQ
• Website: lucebox.com
• Issues: github.com/Luce-Org/lucebox/issues
• Blog: lucebox.com/blog

---

_{Apache 2.0 · Lucebox.com}