Open Standard, Multi-vendor AI Training and Inference with LLMs | Tech Talk | Innovation Selects

Опубликовано 10 октября 2024, 21:00

Build and deploy LLMs with SYCL. Learn about the llm.c and llama.cpp projects and their potential for efficient training and inference on Intel and Nvidia GPUs.

LLMs have become a very popular method of harnessing AI and there are many emerging models available to developers. During this presentation we will talk about different ways to run LLMs that take advantage of the open standard nature of SYCL. The llm.c code from Andrej Karpathy is a simple project for LLM training using CUDA or pure C, and the llama.cpp project has been implemented to run fast inference on a wide range of target architectures. Both of these projects can use a SYCL back end implementation that brings the potential for them to be run on a wide range of targets including Intel and Nvidia GPUs. During this session we will present these two projects, how they are implemented with SYCL, and demonstrate how you can use them to run training and inference of LLMs on multiple vendor targets.

Intel Innovation: intel.ly/46ixwbA

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Llama.cpp Repository and Landing Page: intel.ly/400k4YH

Llm.c Repository and Landing Page: intel.ly/4ez4FDj

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Open Standard, Multi-vendor AI Training and Inference with LLMs | Tech Talk | Innovation Selects