
NVIDIA has achieved significant performance gains with its H100 Tensor Core GPUs and TensorRT-LLM, enabling IT teams to deploy advanced AI models such as Mixtral 8x7B more efficiently. This advancement allows for faster response times and lower costs. Additionally, new techniques in Universal Checkpointing and parallelism are being introduced to boost training efficiency and resilience. These include the ability to change parallelism or GPU count mid-stream, scale down to healthy nodes, and increase throughput by scaling up to elastic nodes. Companies like WEKA and Predibase are also contributing to this field by delivering speed, resilience, and faster iteration times for large language model training, with Predibase achieving a 15x acceleration in less than 15 days.



Faster #training = faster iteration = less time spent achieving high performance on your LLM #finetuning jobs 🏁 🏎️ Check out our deep dive blog to learn how we accelerated fine-tuning by 15x in less than 15 days: https://t.co/LBq0s9E4jm https://t.co/TkhdCR3fRz
#WEKA delivers SPEED + RESILIENCE for #GenAI! 🚀 Dive into our whitepaper and see how we support any model size, reduce #GPU downtime, and boost #LLM training speeds! ⬇️⬇️ https://t.co/dr0oqHbZ72 Unlock 🔐 the power of advanced checkpointing with the WEKA #dataplatform!
Achieving High Mixtral 8x7B Performance with NVIDIA H100 Tensor Core GPUs and TensorRT-LLM. These performance gains enable IT teams to deploy advanced AI models eg Mixtral 8x7B more efficiently, serving more users with faster response times at lower costs https://t.co/MKL6MHOHmR https://t.co/c3kSYlNC9z