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Show HN: Headroom – measure your GPU's true bandwidth ceiling for local AI
Brief published July 21, 2026 · Original source published July 20, 2026
Original reporting by Ar5en1c at ar5en1c.github.io.
Automated brief. Verify important details at the original source.
What happened
Headroom is an open-source browser-based tool that runs a 30-second WebGPU benchmark to measure a GPU's true memory bandwidth ceiling for local AI inference. It targets batch-1 LLM decode workloads, which are memory-bound: token generation speed is constrained by how fast weights can be streamed through the GPU. Beyond bandwidth measurement, the tool includes FP64-gated kernel tests and a canary check designed to surface silent GPU corruption that the author claims remains present in current Chrome builds.
Why it matters
Developers running local LLMs often lack a straightforward way to verify whether their hardware is the bottleneck or whether driver and browser issues are silently degrading output. A lightweight, browser-accessible diagnostic that ties bandwidth numbers directly to tokens-per-second ceilings gives builders a concrete baseline before optimizing model quantization or batching strategies. The corruption canary adds a reliability signal that goes beyond typical benchmark tools.
What to watch
Adoption depends on how broadly WebGPU is supported across target devices and whether the corruption detection holds up across GPU vendors and Chrome versions as the browser runtime evolves.