If mHC scales the way early benchmarks suggest, it could reshape how we think about model capacity, compute budgets and the ...
ByteDance's Doubao AI team has open-sourced COMET, a Mixture of Experts (MoE) optimization framework that improves large language model (LLM) training efficiency while reducing costs. Already ...
Scalable Chiplet System for LLM Training, Finetuning and Reduced DRAM Accesses (Tsinghua University)
A new technical paper titled “Hecaton: Training and Finetuning Large Language Models with Scalable Chiplet Systems” was published by researchers at Tsinghua University. “Large Language Models (LLMs) ...
As the excitement about the immense potential of large language models (LLMs) dies down, now comes the hard work of ironing out the things they don’t do well. The word “hallucination” is the most ...
A new technical paper titled “MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall” was published by researchers at Argonne National Laboratory and ...
A lean team of 15 researchers, many in their twenties, at Sarvam successfully built a 105-billion-parameter foundational LLM from scratch. Spearheaded by Rahul Aralikatte, the young team managed data ...
eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...
On the surface, it seems obvious that training an LLM with “high quality” data will lead to better performance than feeding it any old “low quality” junk you can find. Now, a group of researchers is ...
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