Make your AI cheaper, faster & greener.
One line of code to automatically compress your AI models and make them 2-10x more efficient while maintaining quality.
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Smash Generative AI
GenAI models are great but slow and expensive to scale. Smash them with Pruna to minimize inference latency and memory requirements.
2x faster LLMs
3x less memory to run LLMs
2x faster image generations
2x more energy efficient
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"As billions are invested in AI development, it is imperative to maximize the efficiency and impact of these resources."
Built to work on any AI model on any hardware.
Compress and run your models in one line of code.
Import Pruna and get an API key to make all your models more efficient.
Frequently Asked Questions.
How big are the improvements?
Most models smashed by Pruna tech become 2-10x more efficient. The specific gains really depend on your ML model, target hardware and if you have custom requirements.
How much does it cost?
We're smashing and publishing the most popular AI models for free on Hugging Face. If you need to smash other models or after having trained/finetuned them on your data then you will need a paid API key with us. Pricings depend on various factors but always align with how much you get out of it. Request access to learn more.
Is this for training or for inference?
Our current product makes your AI models more efficient at inference. Use it after training your models and before deploying them on your target hardware. Our next product iteration will make your model training more efficient too and we're eager for people to try it :)
How do you smash AI models?
Our approach integrates a suite of cutting-edge AI model compression techniques. These methods are the culmination of our years of research and numerous presentations at ML conferences including NeurIPS, ICML, and ICLR.
What do you need to smash my AI model?
Our product only needs your AI model and specifications about your target hardware for inference. The smashed models could be less flexible if you have very specific use-case, and that can be worked out with a little support.
Are there any risks?
We aim to maintain the predictive performance of all smashed'AI models, ensuring they're as accurate as their original versions. However, we must clarify that while the practical results have consistently met our goals, we cannot provide a theoretical guarantee of exact match in predictions with the original model. We recommend you test the smashed models on your own internal benchmarks.
Delight your users & build the future
Tell us about your use-cases and get access to make your AIs more efficient for inference.