Ross ROSS = Recommend OSS · open-source software intelligence for agents

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

github.com/microsoft/nnfusion · C++ · MIT (permissive) 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

Full methodology

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

Member repositories

RepositoryRoleHealth v2
microsoft/nnfusionmain23

For agents

markdown · JSON · MCP: product_card(name="microsoft/nnfusion")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem