LLM Training and Inference with Intel Gaudi 2 AI Accelerators

This article describes how Intel Gaudi2 AI Accelerators (Gaudi2) can be used to improve the training and inference process of large language models (LLMs). Gaudi2’s are hardware based solutions that are specifically designed to reduce the overall cost of training and inference for LLMs. This article examines the benefits of using Gaudi2 in the context of LLM training and inference.

In terms of training, Gaudi2 accelerators provide higher performance than cloud GPUs and CPUs, resulting in faster model training. This enables developers to train their models in a shorter amount of time, reducing the overall cost of ownership. As well as improved speed, Gaudi2 also helps to reduce the amount of memory consumed during training by up to 10x, allowing more efficient utilization of resources.

In terms of inference, Gaudi2 accelerators provide higher throughput and lower latency than other solutions, making them well-suited for applications that require faster response times. Gaudi2 can also help to reduce power consumption, providing significant cost savings over other solutions.

Overall, the use of Intel Gaudi2 AI Accelerators is ideal for improving both the training and inference processes of LLMs. By providing higher performance and reduced costs, Gaudi2 accelerators enable developers to speed up model development, minimize resource utilization, and decrease their overall costs. By taking advantage of these benefits, developers can deploy their models faster and with greater accuracy.

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