Dan Alistarh

And presenting a Best Poster award to "Unified Scaling Laws for Compressed Representations" by Andrei Panferov, Alexandra Volkova, @ionutmodo, Vage Egiazarian, @mher_safaryan, and @DAlistarh! Congrats!

Our flagship paper on how far careful quantization can really go in practice got accepted as an oral at ACL 2025 (top 8%)! 🥳 Turns out, old-school methods like GPTQ, SmoothQuant, and RTN are quite good when tuned properly. All of the tricks are already in LLM-Compressor!

This is something I've been working on with some amazing collaborators for a while. Model-software-hardware co-design. Making things run fast on real devices. A lot of learning. And happy to share this with the open-source community and beyond. developers.googleblog.com/en/introducing…

📣 The Journey Matters: Our #ICLR2025 paper shows how to pretrain sparse LLMs with half the size of dense LLMs while maintaining quality. We found that the average parameter count during sparse pre-training predicts quality, not final size. An MIT/Rice/Google/ISTA collab 🧵 1/N