The Inference Hardware Revolution of 2026

_superposition_

Excellent article. I believe the majority of benchmark performance gains moving forward will come from this side of the stack enabling faster iteration/recursion.

geoffbp

> And Anthropic is paying LLM competitor SpaceXAI over a billion dollars per month to lease spare compute I knew of this but not the $ amount. Wow

ninju

Great read. I like how the author uses the analogy of scrabble word creation to describe LLM training but unfortunately the analogy didn't continue to inference and I got lost trying to keep up.

aschla

"If AI inference remains as desirable as Kimball expects, the evolution is likely to follow the same trajectory as the CPU. The CPU didn’t improve along a single axis but instead across simultaneously. Once transistor scaling slowed, chip and system architecture innovations of all kinds proliferated. The list of individual innovations that led to today’s ubiquitous, powerful personal compute could fill dozens of books. A few decades from now, the history of AI inference innovation will show similar depth." Of the areas mentioned in the article, which are the most likely to have the most prominent innovative impact, and what will they entail?

swimwiththebeat

> Tensordyne is expected to accelerate AI inference with a logarithmic number system that leans on a property of logarithms: The log of A times B equals the log of A plus the log of B. So, storing numbers as their exponents lets the chip add where it would otherwise multiply. That matters in silicon because multiplier circuits draw more power and use more die area than adders do. Tensordyne says its rack-scale hardware, called Napier, can produce up to 1,300 tokens per second per user, and can do so while using less than a tenth as much power as comparable Nvidia hardware. Did not know about this cool trick about storing numbers as exponents! Is there a name for this technique? Wouldn’t there be overhead in converting back and forth between the exponent and the number?