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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
Web
Task
model optimizing

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

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Pruna AI

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.

sciencehybridMunichinternship

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

Show more details

Explore AI Career Opportunities

Social Media

discord

Ratings & Reviews

No ratings available yet. Be the first to rate this tool!

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