Companies running large language models face a persistent bottleneck: the memory consumed by key-value caches during ...
Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...
Tether successfully integrated Google’s TurboQuant into the inference engine of its local AI framework, QVAC. It is the ...
Fine-tuning large language models (LLMs) might sound like a task reserved for tech wizards with endless resources, but the reality is far more approachable—and surprisingly exciting. If you’ve ever ...
Two papers on MoE-specific quantization algorithms accepted at a workshop held in conjunction with ICML 2026 Recognition follows Nota AI’s overall win at the NVIDIA Nemotron Hackathon Strengthening ...
Meta Platforms Inc. is striving to make its popular open-source large language models more accessible with the release of “quantized” versions of the Llama 3.2 1B and Llama 3B models, designed to run ...
Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines ...
The proliferation of edge AI will require fundamental changes in language models and chip architectures to make inferencing and learning outside of AI data centers a viable option. The initial goal ...
And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models. Two years ago, Yuri Burda and Harri ...