Running large language models at the enterprise level often means sending prompts and data to a managed service in the cloud, much like with consumer use cases. This has worked in the past because ...
Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Training a large language model (LLM) is ...
Training AI models is a whole lot faster in 2023, according to the results from the MLPerf Training 3.1 benchmark released today. The pace of innovation in the generative AI space is breathtaking to ...
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 ...
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 ...
Performance. Top-level APIs allow LLMs to achieve higher response speed and accuracy. They can be used for training purposes, as they empower LLMs to provide better replies in real-world situations.
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) ...
A large language model (LLM), no matter its inherent architectural sophistication, is only as brilliant as the information it is fed. Businesses looking to take advantage of the potential of LLMs need ...
At the core of HUSKYLENS 2 lies its exceptional computation power, featuring a dual-core 1.6GHz CPU, 6 TOPS of AI performance, and 1GB of memory. All algorithms run directly on-device, ensuring ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback