Hanzo Engine
The inference engine, in one binary
Name a model on Hugging Face and it runs. Engine quantizes it for the hardware it finds, then serves an HTTP API and a web chat from the same process — text, vision, audio, speech, image and embeddings, without a second stack underneath.
One process, laptop to GPU cluster
Chat, an API server, and a benchmark are three commands on the same binary. There is no Python interpreter under any of them.
run, serve, bench
One binary, three verbs. Chat with a model, put it behind an HTTP API, or measure it — the thing you benchmarked is the thing you shipped.
Built to serve, not just to run
Paged attention and continuous batching keep the card busy across concurrent requests. A disk-first KV cache carries a session across a restart, so an agent that reuses a long prefix does not pay for it twice.
More than text
Vision, audio, speech, image and embeddings go through the same modality pipeline as generation, in the same process. A multimodal app is one deployment, not four.
It quantizes on the way in
In-situ quantization of any Hugging Face model, plus GGUF from 2 to 8 bits, GPTQ, AWQ, HQQ, FP8 and bitsandbytes. Ask for a level and it takes a published prebuilt when one exists, or makes one when it doesn't.
The silicon you have
CUDA with FlashAttention and multi-GPU tensor parallelism, Metal on Apple Silicon, or plain CPU. Same engine and same commands on each, and a doctor command that reports what it found.
Tools, and a UI to watch them
It can call MCP servers over a process, HTTP or a WebSocket. The built-in UI at /ui shows reasoning, code execution and plots inline, and edits branch a conversation with its own state. Pass --no-ui and it is a plain API server.