Gen AI Paper Reading: ub 1-bit Quantization with Nanobit and Littlebitv2

About this event

Join us for a paper discussion on "NANOQUANT: Efficient Sub-1-Bit Quantization of Large Language Models" and "LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment" presented by Logan. These papers cover the latest developments in llm quantization where sub 1-bit llm quantization are achieved through combination of sparse matrix approaches and more traditional quantization methods. littlebitv2 (https://arxiv.org/pdf/2603.00042) nanoquant (https://arxiv.org/pdf/2602.06694v3) Silicon Valley Generative AI has two meeting formats. 1\. Paper Reading \- Every second week we meet to discuss machine learning papers\. This is a collaboration between Silicon Valley Generative AI and Boulder Data Science\. 2\. Talks \- Once a month we meet to have someone present on a topic related to generative AI\. Speakers can range from industry leaders\, researchers\, startup founders\, subject matter experts and those with an interest in a topic and would like to share\. Topics vary from technical to business focused\. They can be on how the latest in generative models work and how they can be used\, applications and adoption of generative AI\, demos of projects and startup pitches or legal and ethical topics\. The talks are meant to be inclusive and for a more general audience compared to the paper readings\. If you would like to be a speaker please contact: Matt White