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Show HN: Utilyze – an open source GPU monitoring tool more accurate than nvtop
More accurate GPU utilization measurement than standard tools (nvidia-smi, nvtop, CloudWatch, etc.) by sampling hardware performance counters and reporting compute/memory throughput relative to theoretical limits.
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More accurate GPU utilization measurement than standard tools (nvidia-smi, nvtop, CloudWatch, etc.) by sampling hardware performance counters and reporting compute/memory throughput relative to theoretical limits.
Utilyze addresses a critical flaw in existing GPU monitoring solutions: misleading utilization metrics. The discrepancy between reported 100% utilization and actual 1-10% compute throughput leads to flawed capacity planning and optimization decisions, resulting in significant operational inefficiencies and wasted resources. By providing a more accurate, hardware-level measurement of compute and memory throughput, Utilyze enables organizations to make data-driven decisions on GPU resource allocation. This open-source tool offers a direct solution to a pervasive problem in high-performance computing and AI infrastructure, promising substantial cost savings and performance improvements through precise resource management.
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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What is Utilyze – an open source GPU monitoring tool more accurate than nvtop?
Utilyze – an open source GPU monitoring tool more accurate than nvtop is analyzed by our AI as: More accurate GPU utilization measurement than standard tools (nvidia-smi, nvtop, CloudWatch, etc.) by sampling hardware performance counters and reporting compute/memory throughput relative to theoretical limits.. It focuses on Utilyze addresses a critical flaw in existing GPU monitoring solutions: misleading utilization metrics. The discrepancy between reported 100% utili...
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Data for Utilyze – an open source GPU monitoring tool more accurate than nvtop was aggregated directly from the Hacker News community ecosystem, representing raw developer and early-adopter sentiment.
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The initial public indexing or launch date for Utilyze – an open source GPU monitoring tool more accurate than nvtop within our tracked developer communities was recorded on April 28, 2026.
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Utilyze – an open source GPU monitoring tool more accurate than nvtop has achieved measurable traction, logging over 88 traction score and facilitating 22 recorded discussions or engagements.
Which technical categories define Utilyze – an open source GPU monitoring tool more accurate than nvtop?
Based on metadata extraction, Utilyze – an open source GPU monitoring tool more accurate than nvtop is categorized under topics such as: GPU monitoring, nvidia-smi, nvtop, Weights & Biases.
What are some commercial alternatives to Utilyze – an open source GPU monitoring tool more accurate than nvtop?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as ClawMetry for NVIDIA NemoClaw, which offers overlapping value propositions.
How does the creator describe Utilyze – an open source GPU monitoring tool more accurate than nvtop?
The original author or development team describes the product as follows: "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 t..."
Community Voice & Feedback
I feel like this is tangential to this conversation.Does anyone know of a good tool for "load balancing" usage across local GPUs?Why: I have two RTX3090s (24GB). I've been using nvidia-smi to check usage of my RTX3090. Mostly I'm running llama.cpp with unsloth/Qwen3.6-27B-GGUF:Q4_K_M and getting some pretty decent results for a self hosted LLMs (orchestrated via opencode). I'm surprised at how well it is working for a local model. nvidia-smi is great for determining total VRAM usage and nvtop gives a little more insight.But, I also am doing some experiments with some other non-LLM models (video generation, etc), and want to find a way to timeslice across these GPUs, for example, when my coding is paused.This "Utilyze" tool appears it would get me better insight into usage of one. Can it be scripted to better utilize my GPUs across a diverse load?Any suggestions on whether there are existing projects out there? I thought about vibe coding, but wonder if there is existing art.
Looks good for now.Will further test it.
I believe recent versions of nvtop show efficiency, right?
This sounds super interesting and relevant. I run a small cluster with H100s (often research projects with vLLM) and being able to see not just usage but efficiency would be great.I don't fully get the 100% utilisation vs. 1-10% real compute. Given you rely on telemetry from users to add new models, are you trying to predict how fast a model should be on vLLM, compared to how it runs in practice? What if users tweak some hyperparameters?
Great tool.Just testing for now.Any removal instructions or function for utilyze beyond the manual removal of utilyze & utlz binaries from ~/.local/bin & /usr/local/bin & PATH cleanup for ~/.profile, in particular CAP_SYS_ADMIN capability and reversal for any other changes made?
You mention rocm-smi in your blog post, but you don't actually support AMD gpus?
There's a few dimensions you can look at for gpu load. Probably the easiest indirect metric to watch for gpu load is power usage.But if you really care about this, you should actually profile your application. nsight systems makes this pretty simple to do. Dunno how many actually care about having a TUI.
We just track power utilization.
Hi, many thx, does the os can run on nvidia jetson and orin? Or just for server gpu?
One small suggestion: add more GPU stats to your tool.At the moment (v0.1.3) it is more helpful for compute visualization but keeping track of memory usage/processes/temperature/fan speed/etc. prevent this from becoming a full-on drop-in replacement for `nvidia-smi` for me.
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