Hey everyone, I'm looking for some advice on observability for AI workloads, particularly focusing on GPU inference. I'm working at an AI startup where we handle a ton of images daily, and while we have visibility into CPU and memory usage, as well as APM for our code, we're lacking insight into GPU performance and inference metrics. I'd love to hear from those who have experience running AI models and managing their own infrastructure. What tools or stacks do you use for monitoring GPU load, VRAM usage, processing times, and throughput? Should I consider a DIY solution or leverage a SaaS product? Any recommendations would be greatly appreciated. Thanks!
2 Answers
For observability in your setup, I'd recommend looking into the DCGM and Prometheus stack. It’s a standard choice if you're operating on your own infrastructure. You can use the DCGM Exporter from NVIDIA, which queries the GPU and provides data like utilization, VRAM usage, and more. Pairing this with Grafana allows for great visualization.
For monitoring model performance, instrument your inference server (like FastAPI or Triton) to gather custom metrics, such as time to first token and inference latency. Starting with DCGM Exporter + Grafana is a solid move for free, robust observability, and it’s what many big players use too!
I get where you’re coming from; the GPU observability landscape isn't as mature as CPU/memory. However, from my experience, implementing the DCGM + Prometheus solution provides solid results. It’s a steep learning curve at first, but once you overcome that, you’ll be back on track.
For real production insights, consider how you plan to scale. The challenges usually revolve around integrating metrics effectively and maintaining performance with added observability tools. It’s worth exploring, but I’d suggest starting small and iterating.
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