Making AI smaller, faster, and more accessible.
We build open-source tools that help developers deploy efficient AI models without sacrificing performance.
Founded
GitHub stars
Companies shipping with FasterAI
Compression without compromise.
Modern AI models deliver extraordinary results, and they come with extraordinary costs. State-of-the-art compute requirements lock smaller teams out, strand edge devices, and keep the carbon footprint of AI climbing.
FasterAI Labs closes the gap between cutting-edge compression research and what developers can actually ship. Years of work on pruning, quantization, and distillation, all packaged into libraries that take minutes to learn and a few lines of code to apply.
What used to require a PhD now runs in a single function call.
Three ways we accelerate AI.
Three tools. One goal: deploy models that run anywhere, without giving up accuracy.
Model Compression
Prune, quantize, and distill neural networks to shrink model size by up to 90%, without retraining from scratch.
Lower storage. Cheaper inference.
Inference Acceleration
Optimized architectures and efficient runtimes that deliver 3–5× speedups on any hardware, from cloud GPUs to edge devices.
Real-time UX. Edge deployment.
Open-Source Libraries
Production-ready tools that drop into existing PyTorch workflows in a few lines of code. Full docs, no lock-in.
Apache 2.0. Transparent.
Why it matters.
Efficient models aren't just a technical win. They unlock new products, lower bills, and make AI available to teams and devices that couldn't afford it before.
Lower Costs
Cut cloud compute and infrastructure spend.
Edge-Ready
Run efficient models on mobile and embedded hardware.
Greener AI
Reduce energy use and carbon footprint.
Better UX
Deliver real-time AI with lower latency.
Built in the open.
Every core library ships under the Apache 2.0 license. Use it, fork it, contribute back. No black boxes. No vendor lock-in.