GPU hot

What it does

Real-time NVIDIA GPU monitoring that scales from one machine to dozens with a single Docker command. 500ms updates, web-based dashboard, and zero configuration. Built for developers who need simple GPU monitoring without complex infrastructure.

• UA
  Utilyze – an open source GPU monitoring tool more accurate than nvtopApr 2026 · systalyze.com · ▲128

  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…

• SF
  Software for Remote GPU-over-IP2022 · github.com · ▲138

  We built installable software for Windows & Linux that makes any remote Nvidia GPU accessible to, and shareable across, any number of remote clients running local applications, all over standard networking.

• TC
  TensorDock Core GPU Cloud – GPU servers from $0.29/hr2022 · tensordock.com · ▲147

  Hello HN! I’m Jonathan from TensorDock. After 7 months in beta, we’re finally launching Core Cloud, our platform to deploy GPU virtual machines in as little as 45 seconds! https://www.tensordock.com/product-core Why? Training machine learning workloads at large clouds can be extremely expensive. This left us wondering, “how did cloud ever become more expensive than on-prem?” I’ve seen too many ML startups buy their own hardware. Cheaper dedicated servers with NVIDIA GPUs are not too hard to find, but they lack the functionality and scalability of the big clouds. We thought to…

• 
  L9gpu – GPU telemetry that ties each GPU to the K8s pod or Slurm jobJul 2026 · github.com · ▲20

  GPU Observability with workload attribution. One OTLP agent per node ties hardware metrics (NVIDIA, AMD, Intel Gaudi) to the K8s pod or Slurm job burning the GPU. - last9/gpu-telemetry

• 1T
  15FPS to 60FPS, new GPU real-time flow-based method2020 · youtube.com · ▲112
• NG
  Neurox – GPU Observability for AI Infra2025 · github.com · ▲25

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• SA
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• AS
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  Life & fun · Oct 2025 · autism-simulator.vercel.app

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