Open-Source Library
Make your AI Model Efficient with Pruna
Make your AI Model Efficient with Pruna
Learn about optimization, optimize your own model with multiple State-Of-The-Art algorithms, and deploy it on any platform.
Learn about optimization, optimize your own model with multiple State-Of-The-Art algorithms, and deploy it on any platform.
pip install pruna
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pip install pruna
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pip install pruna
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Pruna Package
Combine 50+ state-of-the-art compression algorithms
(including pruning, quantization, caching, and more!)
and make your own optimized models.
Combine 50+ state-of-the-art compression algorithms
(including pruning, quantization, caching, and more!)
and make your own optimized models.
Combine 50+ state-of-the-art compression algorithms
(including pruning, quantization, caching, and more!)
and make your own optimized models.
Pruna OSS Models
Run 10K+ AI-optimized models in free access on
Hugging Face, covering image, video, and text.
Run 10K+ AI-optimized models in free access on
Hugging Face, covering image, video, and text.
Run 10K+ AI-optimized models in free access on
Hugging Face, covering image, video, and text.
Make Your Own AI Model
Faster, Smaller, Cheaper, Greener!
Pruna AI Optimizing Image &
Video generation models
Make Your Own AI Model
Faster, Smaller, Cheaper, Greener!
By using Pruna OSS, you gain access to the most advanced optimization engine, capable of smashing any AI model with the latest compression methods for unmatched performance.
Flux Kontext
Janus-Pro-7B
Flux Dev
Flux Kontext
Janus-Pro-7B
Flux Dev
Flux Kontext
Janus-Pro-7B
Flux Dev
Learn about all families of compression methods!
Pruning
Pruning removes less important or redundant connections and neurons from a model, resulting in a sparser, more efficient network.
Quantization
Quantization reduces the precision of the model’s weights and activations, making them much smaller in terms of memory required.
Batching
Batching groups multiple inputs together to be processed simultaneously, improving computational efficiency and reducing overall processing time.
Enhancing
Enhancers improve the quality of the model’s output. They range from post-processing to test time compute algorithms.
Caching
Caching is a technique used to store intermediate results of computations to speed up subsequent operations, particularly useful in reducing inference time for machine learning models.
Recovery
Recovery restores the performance of a model after compression.
Factorization
Factorization batches several small matrix multiplications into one large fused operation which, while neutral on memory and raw latency, unlocks notable speed-ups when used alongside quantization.
Distillation
Distillation trains a smaller, simpler model to mimic a larger, more complex model.
Compilation
Compilation optimizes the model for specific hardware.
Distributers
Distributers distribute the model or certain calculations across multiple devices, improving computational efficiency and reducing overall processing time.
Pruna Courses
Learn how to compress, evaluate, and deploy
efficient AI models from theory to practice.
Pruna Materials
Stay up to date with the most recent
AI optimization literature.
Run 10K+ AI-optimized models in free access on
Hugging Face, covering image, video, and text.
Learn more about Pruna's OSS library with our blog articles
Built with Pretzels & Croissants 🥨 🥐
Built with Pretzels & Croissants 🥨 🥐