Powering AI with the World's Largest Computer Chip with Joel Hestness - #684
Published:May 13, 2024 19:58
•1 min read
•Practical AI
Analysis
This podcast episode from Practical AI features Joel Hestness, a principal research scientist at Cerebras, discussing their custom silicon for machine learning, specifically the Wafer Scale Engine 3. The conversation covers the evolution of Cerebras' single-chip platform for large language models, comparing it to other AI hardware like GPUs, TPUs, and AWS Inferentia. The discussion delves into the chip's design, memory architecture, and software support, including compatibility with open-source ML frameworks like PyTorch. Finally, Hestness shares research directions leveraging the hardware's unique capabilities, such as weight-sparse training and advanced optimizers.
Key Takeaways
- •Cerebras is developing custom silicon (Wafer Scale Engine 3) for machine learning, specifically targeting large language models.
- •The episode compares Cerebras' hardware to other AI solutions like GPUs and TPUs, highlighting its unique design and memory architecture.
- •The discussion covers software support, including compatibility with open-source ML frameworks and research directions leveraging the hardware's capabilities.
Reference
“Joel shares how WSE3 differs from other AI hardware solutions, such as GPUs, TPUs, and AWS’ Inferentia, and talks through the homogenous design of the WSE chip and its memory architecture.”