microsoft/nnfusion
A flexible and efficient deep neural network (DNN) compiler that generates high-performance executable from a DNN model description. observed · 2026-09-03
Health v2 · maintenance only
23/100
- Activity 0
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2345
- days_rel: n/a
- days_push: 713
- n_releases_24m: 0
Adoption not part of the score
1002 stars · 166 forks observed · 2026-09-03
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
NNFusion is a flexible and efficient deep neural network (DNN) compiler that generates high-performance executables from model descriptions such as TensorFlow frozen models and ONNX files. It performs full-stack optimizations including kernel fusion, memory layout optimization, and ahead-of-time code generation with minimal runtime dependencies.
Use cases
- compile a frozen TensorFlow or ONNX model into a fast standalone executable
- deploy a pre-trained DNN model without framework dependencies
- speed up inference of a pre-defined deep learning model on CUDA or ROCm GPUs
- experiment with custom compiler optimizations for specific neural network models
- generate human-readable source code from a model to hand-tune kernels
- run distributed parallel training with SuperScaler integration
When to choose
- you need framework-free, low-overhead deployment of TensorFlow or ONNX models on GPUs
- you want ahead-of-time compilation instead of runtime graph execution
- you are a researcher prototyping compiler optimizations for DNNs
- you need kernel fusion and memory layout tuning for inference performance
When to avoid
- you need broad model format support beyond TensorFlow and ONNX
- you primarily target Windows or macOS, since support is focused on Ubuntu with CUDA
- you want an actively evolving project, as development appears to be in maintenance mode
- you need dynamic-shape or training-focused compilation without extra tooling
Facets
cli-tool · maturity maintenance
compiler machine-learning deep-learning llm-inference gpu-computing developer-tools machine-learning deep-learning compilers gpu-computing developer-tools cpp dnn-compiler code-generation onnx tensorflow kernel-fusion ahead-of-time-compilation inference linux docker gpu
1 source
- readme: https://github.com/microsoft/nnfusion · fetched 2026-09-03 · 6df70f16ef67
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| microsoft/nnfusion | main | 23 |
For agents
markdown · JSON · MCP: product_card(name="microsoft/nnfusion")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem