Headroom – measure your GPU's true bandwidth ceiling for local AI
Detects the subgroup bug that silently breaks WebGPU LLMs before you waste hours debugging.

Throughput-based GPU metrics expose 1% real utilization when nvtop reports 100%.
ML engineers and AI infrastructure teams
nvtop · nvidia-smi · Datadog GPU monitoring
This becomes a problem when teams rely on that metric for capacity planning or optimization decisions, it can make underutilized systems look saturated.
We're releasing an open-source (Apache 2.0) tool, Utilyze, to measure GPU utilization differently. It samples hardware performance counters and reports compute and memory throughput relative to the hardware's theoretical limits. It also estimates an attainable utilization ceiling for a given workload.
GitHub link: https://github.com/systalyze/utilyze
We'd love to hear your thoughts!
Detects the subgroup bug that silently breaks WebGPU LLMs before you waste hours debugging.
Per-app wattage attribution using RAPL and GPU counters when other monitors only show component totals.
Hardware-attested GPU enclaves with crypto payments eliminate operator trust requirements.
Comprehensive AGP computation with LBGI, HBGI, GRADE—medical-grade metrics in open source.
Finally, an actual order book for GPU hours instead of a static listing wall.
Privacy-focused demo estimates measurements from two photos without storing images.