Most inference stacks are a collection of pipelines: one for language models, another for speech, another for image generation, another for video, each with its own loader, its own memory habits and its own list of supported GPUs. NeuroBrix takes the opposite route. One engine runs every model. Version 0.6 is its first stable release.

pip install neurobrix

It runs on Linux, Windows and macOS, on NVIDIA, AMD, Intel and Apple GPUs — wherever Triton runs.

How NeuroBrix works

49 models, 9 families, one entry point

NeuroBrix 0.6 runs 49 models across 9 families: language and code models (Qwen3-30B-A3B, Qwen3-Coder, DeepSeek-Coder-V2-Lite, DeepSeek-MoE), vision-language and omni models (GLM-4.1V, Qwen3-VL, Qwen3-Omni, Janus-Pro, MiniCPM-o), speech recognition (Whisper, Parakeet), speech understanding (Voxtral), text-to-speech (Kokoro, VibeVoice, Orpheus), image generation (Sana 1K and 4K, PixArt, Flex.1), video generation (Wan 2.1 and 2.2, CogVideoX, Mochi, Allegro, Open-Sora 2) and upscaling (Real-ESRGAN, HAT, SwinIR, Swin2SR).

49 models, 9 families, one engine

They all go through the same command. The family is read from the model, so run asks for the inputs that family needs:

neurobrix import TinyLlama/TinyLlama-1.1B-Chat-v1.0 neurobrix run --model TinyLlama-1.1B-Chat-v1.0 --prompt "Explain recursion." --max-tokens 256 neurobrix run --model whisper-large-v3-turbo --audio recording.wav neurobrix run --model Kokoro-82M --prompt "Hello from NeuroBrix." --output speech.wav neurobrix run --model Flex.1-alpha --prompt "a lighthouse at dusk" --steps 20 --seed 42 --output lighthouse.png neurobrix run --model CogVideoX-2b --prompt "a hot air balloon over mountains" --num-frames 17 --output balloon.mp4

The browsable list lives at neurobrix.es/models.

One container, no per-model code

Every model ships as a .nbx container: one file format that holds the model's graph and its weights. The engine reads the container and executes it. There is no if model == ... anywhere in the runtime, no model-specific pipeline to maintain, and adding a model does not mean writing a new code path. That is what lets one small team run 49 models through a single entry point.

Two engines in one package

NeuroBrix carries two execution engines that share the container, the plan and the flow, and share no compute code:

  • The Triton engine, built on nbxtensor, NeuroBrix's own tensor library. It does not use PyTorch at all — not at import, not at run time.
  • An optional PyTorch engine, for those who already have PyTorch: pip install "neurobrix[pytorch]".

By default NeuroBrix uses PyTorch if it is installed and the Triton engine otherwise. The difference shows on disk. Measured on a clean machine, the light install is 1.48 GB; with PyTorch it is 6.82 GB.

The stack without PyTorch

Prism: placement from facts, not guesses

Before anything runs, Prism, NeuroBrix's memory and placement planner, decides how the model sits on your hardware: on a single GPU, across several GPUs, streamed in pieces, or with its largest operations tiled. It works from a hardware profile — a file of facts about your devices — rather than from heuristics baked into code. When the machine is busy, it says so and tells you what it chose instead.

Speed

Speed comes from kernels that are measured, not assumed. In the global benchmark of 2026-11-05 on V100 16 GB GPUs, the Triton engine was faster than the PyTorch engine on 18 of the 49 models, up to 5x on Janus-Pro-7B (212.6 s down to 41.8 s).

Two pieces make that possible:

  • Certified kernel tuning. Every kernel setting is certified once per GPU class — checked against a float64 oracle, then timed — instead of autotuning on your machine. 18,552 settings are certified for Apple GPUs alone, and anyone can certify their own GPU and share it by pull request.
  • V100 tensor cores, back. Triton compiles matrix products to FMA on Volta, which leaves the tensor cores of a V100 idle. nbxtensor brings them back with hand-written m8n8k4 kernels, chosen per operation by measurement.

Validated the boring way

Every release runs all 49 models on both engines through the real entry point — the same neurobrix run you type. For 0.6.0 that was 196 of 196 cells on 4x V100. Outputs are compared byte for byte with outputs that were judged by eye (text read, images and videos looked at, audio transcribed), and runs are deterministic: the same inputs give identical outputs.

Apple Silicon

On a Mac, PyPI has no Triton build. NeuroBrix maintains its own Triton build for Apple GPUs, NeuroBrix/triton-ext, installed in one line:

pip install neurobrix python -m nbxtensor install-metal neurobrix doctor

Try it

Found a model that misbehaves on your GPU? Open an issue — that is exactly the feedback this release is for.

— Hocine Benkelaya, NeuroBrix