The Challenges
ML engineers prioritize frequent re-training and deploying efficient models.
Compute budgets must stay under control while balancing resource constraints.
All this happens with shifting business priorities and new project demands.
New models, new architectures and new evaluation techniques emerge at a rapid pace.
Staying updated requires significant time and effort, leaving little room for AI efficiency.
AI Engineers often face trade-offs between experimentation and practical deployment.
Single-method optimizations deliver only 5–15% gains vs. 2–5x with advanced compression combination.
Tools like TensorRT or TorchCompile require long setup and implementation times.
Delaying optimization adds complexity in the development cycle, making it harder to achieve efficiency when it’s most needed.
The Solution
No need to manually tweak models for every serving platform or inference server.
Use Pruna AI as your AI co-pilot to optimize any model with multiple compression methods.
It's simple: a Python package and 3 core functions (Config, Smash & Eval).
Compatible with Docker for deployment anywhere.
The AutoML feature recommends the optimal methods mix for your setup.
What They Say About Us
"We trust Pruna AI’s expertise to take care of model optimization, so we can focus our R&D resources on what that sets us apart."
Mikhail Andreev, Sr. Manager, Applied Science @ Zillow | Co-Founder @ Virtual Staging AI (acq.)