Utilyze – an open source GPU monitoring tool more accurate than nvtop
The standard GPU utilization metric reported by nvidia-smi, nvtop, Weights & Biases, Amazon CloudWatch, Google Cloud Monitoring, and Azure Monitor is highly misleading. It reports the fraction of time that any kernel is running on the GPU, which means a GPU can report 100% utilization even if only a small portion of its compute capacity is actually being used. In practice, we've seen workloads with ~1–10% real compute throughput while dashboards show 100%.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/utilyzeWe'd love to hear your thoughts!
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Open-source GPU monitoring tool that measures actual compute throughput instead of misleading kernel runtime metrics.
The pain
The gap
Build angle
Strengths
- Solves a real, specific technical problem with measurable ground truth (hardware performance counters don't lie)
- Addresses a pain point across the entire ML infrastructure stack, relevant to any company running GPUs
- Open-source positioning removes adoption friction and builds credibility in developer community
- Clear technical differentiation: performance counter sampling vs. kernel runtime tracking
- Timing advantage: GPU utilization is becoming a bottleneck as teams scale ML workloads
Risks
- Open-source tool alone has no revenue model and limited defensibility, competitors can fork or cloud providers can integrate
- Adoption requires teams to change their monitoring workflows and trust a new metric; inertia is high
- Hardware performance counter APIs vary across GPU vendors (NVIDIA, AMD, Intel); tool may not generalize
- Market size unclear: how many teams actually care enough about utilization accuracy to switch tools?
- No evidence yet that wrong capacity planning decisions are frequent enough or costly enough to drive adoption
- Requires deep systems knowledge to maintain; small team risk if founders move on
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Five lenses, one composite. How scoring works
The angle
No market research recorded for this idea yet.
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