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AI chips for ultra-fast training and inference
HQ: San Francisco, CA, United States | Founded: 2026 | Employees: 3 | Stage: Seed (Y Combinator S26) | Website: https://baudlabs.ai
Baud is building AI hardware for training, fine-tuning and running frontier models, claiming large gains over existing chips. Its core idea is a new way of representing numbers in neural networks that involves no multiplication: chip designs drop multiplier circuits from both training and inference, and model weights shrink more than 10x without losing capability. Because the cores need no multipliers they are far smaller and simpler than GPU tensor cores or the processing elements inside TPU-style chips, so more computing power and fast memory fits on each piece of silicon, and more weights move for the same memory bandwidth. The stated catch is that models must be trained in this representation, or start from a base model already using it, which is why Baud targets training rather than a
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