Lucebox is a plug-and-play computer built for running local AI models and agents at full speed. Inside the custom chassis, a Ryzen AI MAX+ 395 with 128GB of unified LPDDR5X memory is paired with an RTX 3090, and the two work together through an open-source inference engine hand-tuned for exactly this hardware.
The architecture is what makes it fast. Large models live in the 128GB unified memory tier, while the 3090's high-bandwidth VRAM acts as a fast tier. Speculative decoding (DFlash) and speculative prefill (PFlash) bridge the two, producing inference speeds up to 10x higher than llama.cpp on the same silicon and beating machines like the Mac Studio and DGX Spark at a fraction of their effective cost.
Getting started takes minutes, not weeks. The whole stack comes pre-installed, and a single CLI command deploys any open model. There is no driver configuration, no quantization trial and error, no environment debugging. The software is fully open source on GitHub (Luce-Org/lucebox-hub), with thousands of stars and dozens of contributors improving the kernels in the open.
For developers and teams, the payoff is threefold: top-of-class tokens per second at $4,900, complete data privacy since nothing touches the cloud, and a fixed hardware cost that replaces ever-growing API bills. If you want to run agents around the clock on hardware you own, Lucebox is the computer for it.
A startup from San Francisco, the United States that is founded by Alessandro Puppo.
Hybrid memory architecture
128GB of LPDDR5X unified memory on the Ryzen AI MAX+ 395 holds large models, while the RTX 3090's 24GB of fast GDDR6X serves as a high-bandwidth tier. Speculative decoding across the two tiers delivers up to 10x faster inference than comparable single-tier machines.
Custom open-source inference engine
Lucebox ships with hand-tuned CUDA kernels, DFlash speculative decoding, and PFlash speculative prefill (10x faster than llama.cpp), all open source with 2,000+ GitHub stars and an active contributor community.
One-command model deployment
A single CLI pulls, configures, and serves any open model. No driver hunting, no quantization guesswork, no environment setup. Plug it in and run inference in minutes.
Pre-tuned for the exact hardware
Unlike generic builds, the entire software stack is optimized for this specific chip pairing, so you get the full performance the silicon is capable of, out of the box.
I am the founder of Lucebox, focused on making local AI faster, more accessible, and easier to deploy. My goal is to give developers a powerful system that runs AI models efficiently while keeping data private. We are building hardware and software that help teams unlock the full potential of local AI.
CUDA 12+, C++17, Python 3.10+, GGUF, DFlash & PFlash, NVIDIA RTX 3090, AMD Ryzen AI MAX+ 395, Linux
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