Gemini Executive Synthesis

Utilyze: An open-source GPU monitoring tool.

Technical Positioning

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.

SaaS Insight & Market Implications

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.

Proprietary Technical Taxonomy

Raw Developer Origin & Technical Request

Hacker News Apr 28, 2026

Show HN: 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: github.com/systalyze/utilyze... love to hear your thoughts!

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Developer Debate & Comments

xrd • Apr 27, 2026

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.

vogje01 • Apr 27, 2026

Looks good for now.Will further test it.

apitman • Apr 27, 2026

I believe recent versions of nvtop show efficiency, right?

Cynddl • Apr 27, 2026

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?

SilentM68 • Apr 27, 2026

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?

latchkey • Apr 27, 2026

You mention rocm-smi in your blog post, but you don't actually support AMD gpus?

uberduper • Apr 27, 2026

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.

jhgg • Apr 27, 2026

We just track power utilization.

nawi • Apr 27, 2026

Hi, many thx, does the os can run on nvidia jetson and orin? Or just for server gpu?

xtimecrystal • Apr 27, 2026

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.

Frequently Asked Questions

Market intelligence mapped to Utilyze: An open-source GPU monitoring tool..

How is Utilyze: An open-source GPU monitoring tool. positioned in the market?

Based on our AI analysis of the original developer request, its primary technical positioning is: 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.

How is the developer community reacting to Utilyze: An open-source GPU monitoring tool.?

Yes, we have tracked 22 direct responses and active debates regarding this specific topic originating from Hacker News.

What architecture is tied to Utilyze: An open-source GPU monitoring tool.?

Our proprietary extraction maps Utilyze: An open-source GPU monitoring tool. to adjacent architectural concepts including GPU monitoring, nvidia-smi, nvtop, Weights & Biases.

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Cross-Market Term Frequency

Quantifies the cross-market adoption of foundational terms like Apache 2.0 and nvidia-smi by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.