Sources
Loading...
Additional media
Loading...
DeepNewz, mobile.
People-sourced. AI-powered. Unbiased News.
In 2024, product and engineering teams at various companies have made significant advancements in AI model training, optimization, and deployment for Large Language Models (LLMs) on CPUs and GPUs. Databricks Mosaic Research team, led by mvpatel2000, davisblalock, Saaketh Narayan, and Cheng Liang, has focused on improving training speed and benchmark results for LLMs and genAI models, achieving over 700 TFLOPs on H100s with linear scaling. The teams emphasize the importance of developing custom GenAI solutions on platforms like Databricks and DbrxMosaicAI for enterprises.
🚀 Dive into our latest blog post on LLMs Speed Benchmark and uncover the nuances of different Inference libraries! 📊 We've put Gemma 7B, Llama-2 7B, and Mistral 7B to the test across a range of scenarios to bring you comprehensive insights. Our detailed analysis covered… https://t.co/82Ac0pqWOI
Training LLMs is tough work and lots can go wrong. Scale 📈is hard and things break 💔, often. Listen to @davisblalock and me break it down and get some insights💡into why developing custom GenAI on @databricks + @DbrxMosaicAI is the best solution for enterprises! https://t.co/DnejuUHkiL
The @databricks Mosaic Research team is committed to building the best possible training stack for #LLMs and #genAI models. @mvpatel2000, @davisblalock, Saaketh Narayan, and Cheng Liang write about our latest benchmark results and training speedup methods here:…
