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 ...
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 ...
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 ...
If mHC scales the way early benchmarks suggest, it could reshape how we think about model capacity, compute budgets and the ...
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 ...
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 ...
Contrary to long-held beliefs that attacking or contaminating large language models (LLMs) requires enormous volumes of malicious data, new research from AI startup Anthropic, conducted in ...
NVIDIA is now promoting how much people companies that want to train an AI LLM model can save when using the company's GPU. According to their estimates, the price of training their LLMs would drop ...
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