AI inference needs bandwidth-first hardware, one post argues
The post highlights Positron, saying it ships Atlas inference machines and is building Asimov, a custom chip that puts model weights next to the multipliers.
TLDR
A September 11 post argues that inference—running a large language model—is limited by memory bandwidth rather than compute. Because model weights are barely reused during the calculations, it proposes designing hardware around streaming those weights, then adding only as much compute as that stream can feed.
The post points to Positron as an example, describing its Atlas machine and Asimov chip under development. It says the company uses commodity memory instead of high-bandwidth memory and offers an OpenAI-compatible API, making its machines look more like a web service than a programmable GPU.
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