Tim Dettmers develops computationally efficient methods for deep learning. He is a leader in quantization: coarse graining of large neural networks to increase speed and reduce hardware requirements.
Tim developed 4-and 8-bit quantizations enabling training and inference with large language models on affordable GPUs and CPUs - i.e., as commonly found in home gaming rigs.
Tim and Steve discuss: Tim's background and current research program, large language models, quantization and performance, democratization of AI technology, the open source Cambrian explosion in AI, and the future of AI.
Tim's site: https://timdettmers.com/
Tim on GitHub: https://github.com/TimDettmers
0:00 Introduction and Tim’s background
18:02 Tim's interest in the efficiency and accessibility of large language models
38:05 Inference, speed, and the potential for using consumer GPUs for running large language models
45:55 Model training and the benefits of quantization with QLoRA
57:14 The future of AI and large language models in the next 3-5 years and beyond