Inference is becoming more important than pre-training in AI chips, including how pre-fill and decode work and why more compute is shifting toward serving models.
Today we walk through Etched’s ASIC system for transformer inference, its claims around efficiency and throughput, and the tradeoff between specialization and general-purpose GPUs like NVIDIA’s.
We also look at custom chip efforts from companies like OpenAI, Google, and Amazon, and argues that inference demand may keep growing as AI agents and long-running workloads expand.
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TIMESTAMPS
0:00 Inference’s New Frontier
2:14 Training Versus Inference
5:19 Etched’s Bold Bet
7:58 Building the Whole Rack
10:48 TSMC and the Hard Problems
13:29 Why Inference Matters
14:59 The Transformer Risk
17:02 OpenAI’s Jalapeno Chip
18:59 Why Accelerators Keep Winning
22:28 The Market Is Underpricing It
23:10 NVIDIA Is Still in the Game
24:56 Vertical Integration Wins
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RESOURCES
Josh: https://x.com/JoshKale
Ejaaz: https://x.com/cryptopunk7213
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Not financial or tax advice. See our investment disclosures here:
https://www.bankless.com/disclosures
Josh works with Anthropic as a contractor. All views expressed are his own and do not represent Anthropic, its leadership, or its affiliates. Nothing in this episode is investment advice.
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