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OpenAIDecember 13, 2025

Circuit Sparsity

Open Source
ML
Infrastructure
Testing

Explore Circuit Sparsity

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### TL;DR

Circuit Sparsity is a set of open tools developed by OpenAI to connect weight-sparse models and dense baselines through activation bridges. These tools aim to enhance the interpretability and efficiency of transformer models by enforcing sparsity during training, resulting in more compact and interpretable circuits.

Key Insights & Metrics

Pricing
Free
Cost structure
Version
1.0
Current release version
Hardware
GPU recommended for training (A100/H100)
Compute requirements
Category
Open Source
Licensing model
Region
United States
Primary region

Key Features

  • Enforces weight sparsity during training
  • Maintains consistent fraction of nonzero elements across matrices
  • Achieves approximately 1 in 1000 nonzero weights in the sparsest models
  • Enforces mild activation sparsity with about 1 in 4 node activations nonzero
  • Provides tools for connecting weight-sparse models and dense baselines through activation bridges

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