NEURAX
The Analytical Compiler for Neural Architectures
NEURAX predicts the cost, memory, and performance of neural network architectures before training — in under 50 ms, with zero GPU, and fully deterministically.
About
NEURAX is an analytical compiler for neural network architectures. Whereas training frameworks (PyTorch, TensorFlow) execute models and runtime compilers (IREE, OpenXLA) lower them for execution, NEURAX operates at design time: it answers the questions you need resolved before committing GPU resources.
- Will this architecture fit in VRAM?
- What is the training cost on 8x H100?
- Where are the memory bottlenecks?
- Is inference stable? What is the hallucination risk?
- Which parallelism strategy is optimal?
All in under 50 ms. Zero GPU required. Fully deterministic.
Key capabilities
- 11 architecture families — Transformer, CNN, MoE, SSM, Diffusion, GNN, GAN, RL, SNN, RNN, Experimental.
- 680+ configurable blocks and 88 reference templates.
- 10-pass analytical IR pipeline producing 55+ metrics.
- MLIR / LLVM 18 compiler backend with 13 custom dialects.
- Visual design canvas, AI copilot agent, Inference Intelligence and Time Machine.
- Export to PyTorch, ONNX, Triton, MLIR, Rust/Burn, JSON and Network Graph.
Documentation
Use the sidebar to navigate the full documentation set. Start with the Architecture & Design chapter to understand how NEURAX works internally, then the API Reference and Deployment Guide to run it yourself.