#  Colloquium Series: Yury Polyanskiy 

 



####  calendar\_today Date and Time 

 **March 24, 2025** 

 12:00PM - 01:00PM EDT 

####  pin\_drop Location 

 **Science Center 316**  



 

 



 

 Our upcoming event for the Statistics Department Colloquium Series is scheduled for Monday, March 24 from 12:00 – 1:00pm (ET) and will be an in-person presentation Science Center **300H**. Lunch will be provided to guests following the talk. This week's speaker will be Yury Polyanskiy of MIT's Electrical Engineering and Computer Science.

 **Optimal Quantization for LLMs and Matrix Multiplication**  
The main building block of large language models is matrix multiplication, which is often bottlenecked by the speed of loading these matrices from memory. A number of recent quantization algorithms (SmoothQuant, GPTQ, QuIP, SpinQuant etc) address this issue by storing matrices in lower precision. We derive optimal asymptotic information-theoretic tradeoff between accuracy of the matrix product and compression rate (number of bits per matrix entry). We also show that a non-asymptotic version of our construction (based on nested Gosset lattices and Conway-Sloan decoding), which we call NestQuant, reduces perplexity deterioration almost three-fold compared to the state-of-the-art SpinQuant (on 4-bit quantized Llama-3-8B).   
Based on a joint work with Or Ordentlich (HUJI), Eitan Porat and Semyon Savkin (MIT EECS).



 

 

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 Attachments- [  image  yurypolyanskiy.jpg ](/sites/g/files/omnuum10116/files/yurypolyanskiy.jpg)
 
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