GPUSprint
Measure GPU utilization across vendors NVIDIA, AMD, TPUs ...
What it does
Advanced monitoring and control for multi-cloud accelerators, including NVIDIA, AMD, Tenstorrent, Google TPUs, and AWS chips. Deploy a single DaemonSet to Kubernetes clusters and understand how effectively your research teams are using accelerators and how many resources they consume.
Does the same job
all alternatives →- TLTune LLaMa3.1 on Google Cloud TPUs2024 · github.com · ▲189
Hey HN, we wanted to share our repo where we fine-tuned Llama 3.1 on Google TPUs. We’re building AI infra to fine-tune and serve LLMs on non-NVIDIA GPUs (TPUs, Trainium, AMD GPUs). The problem: Right now, 90% of LLM workloads run on NVIDIA GPUs, but there are equally powerful and more cost-effective alternatives out there. For example, training and serving Llama 3.1 on Google TPUs is about 30% cheaper than NVIDIA GPUs. But developer tooling for non-NVIDIA chipsets is lacking. We felt this pain ourselves. We initially tried using PyTorch XLA to train Llama 3.1 on TPUs, but it was rough: xla…
- SFSoftware 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.
- ATAttaching to a virtual GPU over TCP2024 · thundercompute.com · ▲344
We developed a tool to trick your computer into thinking it’s attached to a GPU which actually sits across a network. This allows you to switch the number or type of GPUs you’re using with a single command.
- UAUtilyze – 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…
- TCTensorDock 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…
- VAvGPU and SR-IOV on consumer GPUs2021 · arccompute.com · ▲203
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