Guillaume Verdon joins MTS to discuss the launch of the open-source Z1T model family, the co-evolution of sparse transformer algorithms with thermodynamic hardware, and how sparse probabilistic processing achieves a 100x efficiency gain for data center inference workloads. Check out our sponsor Lovable: https://lovable.dev. Turn ideas into software people love.

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Timestamps:

(00:00) Introduction

(00:01) Training Transformers for Z1

(00:04) Achieving 140X Efficiency Gains

(00:07) Disaggregating the Traditional Compute Landscape

(00:09) Open Sourcing the Training Recipe

(00:12) Modifying Transformer Architecture Primitives

(00:14) Breaking Free From Hardware Lotteries

(00:15) Scaling Chip Connectivity and Sparsity

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Sparse Analog AI Chips Cut Data Center Energy 100x | Guillaume Verdon

MTS

Sparse Analog AI Chips Cut Data Center Energy 100x | Guillaume Verdon