Pruna AI
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About
Pruna AI is an AI Optimization Inference Framework for ML teams seeking efficiency and productivity gains. It combines compression algorithms for AI models and works with any AI model, supporting various serving platforms. Pruna helps make AI models faster and cheaper. It is compatible with ComfyUI, TritonServer, SageMaker, and Replicate. The tool focuses on optimizing AI solutions to be smaller, cheaper, faster, and greener, making efficient AI more accessible.
Platform
Task
Features
• open-source
• quality evaluation metrics integrated (lpips, ssim, pnrsr…)
• ready to use with loras
• supports all serving platforms
• combines all optimization algorithms
• works with any ai model
• combines compression algorithms for ai models
• ai inference optimization framework
FAQs
Can I use Pruna for free?
Forever.
How much does it cost?
ML teams rely on Pruna Pro to build more efficient models and save time in deployment with agents.
How to estimate the number of hours I need?
Ask all your product questions. Set your Pruna environment. Understand how our pricing works.
Pricing Plans
Open-Source
Free Plan• Works with any models (image/video gen, SLM/LLM, computer vision, audio…)
• All OSS optimization algorithms (pruning, caching, batching, quantization, compilation, distillation…)
• Combination of optimization algorithms
• All OSS evaluation metrics (LPIPS, SSIM, PNRSR…)
• Compatibility TritonServer, ComfyUI, GPU, Cloud & OnPrem deployment
• Discord Community Support
Pro
USD0.40 / h• All proprietary optimization algorithms
• Quality recovery
• Optimization Agent
• Evaluation Agent
• Implementation services
• Dedicated Slack channel
Enterprise
Unknown Price• Custom evaluation metrics
• Custom Integration
• Multi-GPU
• CPU
• Edge devices
• Service Level Agreement (SLA)
• Training for ML Teams
• Early roadmap access
Job Opportunities
Working Student / Master Thesis / Internship
Pruna AI is an AI Optimization Inference Framework for ML teams. It combines compression algorithms to make AI models faster and cheaper, supporting various platforms and models.
Education Requirements:
A completed B.Sc. in computer science or related fields
Completed coursework on machine learning and/or deep learning
Experience Requirements:
Foundational knowledge in machine learning algorithms
Experience with the PyTorch deep learning framework
Experience with the Python programming language
Ability to read, understand, reimplement and critique research publications
Experience with or coursework about compression methods like quantization, pruning, and compilation
Responsibilities:
Understand and implement compression methods from open-source projects and research papers
Integrate these methods into our compression tool, ensuring they are user-friendly and effective
Adapt and extend successful methods to support various architectures and use-cases
Conduct thorough testing to ensure the reliability and robustness of the compression tool
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