AI will need less HBM (high bandwidth memory) because flash memory unification is changing training and inference. This episode of the Tech Field Day podcast features Sebastien Jean from Phison, Max Mortillaro, Brian Martin, and Alastair Cooke. Training, fine-tuning, and inference with Large Language Models traditionally use GPUs with high bandwidth memory to hold entire data models and data sets. Phison’s aiDaptiv+ framework offers the ability to trade lower cost of infrastructure against training speed or allow larger data sets (context) for inference. This approach enables users to balance cost, compute, and memory needs, making larger models accessible without requiring top-of-the-line GPUs, and giving smaller companies more access to generative AI.
Pushing the Boundaries of AI Performance, Scale, and Innovation at AI Infrastructure Field Day 3
Tech Field Day is heading back in Santa Clara, California on September 10th and 11th for AI Infrastructure Field Day 3. You can watch live on the Tech Field Day website, LinkedIn Page, or Techstrong TV to see how the boundaries of performance, scalability, and innovation are being pushed by our presenting companies. The event […]
Early Adoption of Generative AI Helps Control Costs with Signal65
If you haven’t already, start working with Generative AI now and make sure to control your ongoing costs. This episode of the Tech Field Day podcast features Russ Fellows, Mitch Lewis, and Brian Martin, all from Signal65, and is hosted by Alastair Cooke. Generative AI is delivering value to businesses of all sizes, but significant evolution in models and technologies remains before maturity is achieved. Experimentation is essential to understand the value of new technologies, starting with cloud resources or small-scale on-premises servers. Business value is derived from the inference stage, where AI tools generate actionable information for users. Generative AI is like a knowledgeable and well-intentioned intern; someone more senior must ensure AI is given good instructions and check their work. In production, grounding and guard rails are vital to keep your AI an asset, not a liability.
