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WorkloadTruth
Verify if a GPU job is training or idling
WorkloadTruth is an Apache 2.0 CLI and MCP server that classifies GPU workloads as training, inference, or idle entirely from telemetry like utilization, memory, and power. No code changes or self-reporting are needed. We ship a hash-chained audit log and an evasion benchmark. Unlike NVIDIA DCGM that exposes raw metrics or run:ai that trusts user labels, our approach classifies actual behavior.
As AI infrastructure scales, gaining true visibility into GPU clusters has become a massive headache. Most monitoring dashboards give you raw, out-of-context metrics. You might see a GPU pinned at 100% utilization, but you have no idea what it is actually doing. We were tired of playing a guessing game to figure out if a node was crunching through a training job or serving user inference requests.
We built WorkloadTruth to eliminate this blind spot. It is designed to classify GPU workloads strictly as either inference or training based solely on telemetry data. By looking at hardware behavior signatures (rather than application code), you get the ground truth of what your GPUs are doing.
Initially, we were going to build this as a standalone monitoring script or a traditional DevOps dashboard widget. But while building the classification logic, we realized that the modern developer workflow is shifting towards AI agents. Instead of making users check yet another dashboard, we evolved the project into a Model Context Protocol (MCP) server.
By exposing these classification capabilities via MCP tools, you can now seamlessly connect WorkloadTruth to clients like Claude Desktop or Cursor. Now, you can just ask your AI assistant what your GPUs are doing, and it can natively read the telemetry to give you an answer.
We’d love to hear your feedback, feature requests, or how you plan to integrate this into your own AI infrastructure.
About WorkloadTruth on Product Hunt
“Verify if a GPU job is training or idling”
WorkloadTruth was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #124 on the daily leaderboard. WorkloadTruth is an Apache 2.0 CLI and MCP server that classifies GPU workloads as training, inference, or idle entirely from telemetry like utilization, memory, and power. No code changes or self-reporting are needed. We ship a hash-chained audit log and an evasion benchmark. Unlike NVIDIA DCGM that exposes raw metrics or run:ai that trusts user labels, our approach classifies actual behavior.
On the analytics side, WorkloadTruth competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how WorkloadTruth performed against the three products that launched closest to it on the same day.
Who hunted WorkloadTruth?
WorkloadTruth was hunted by Sourav Nandy. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of WorkloadTruth including community comment highlights and product details, visit the product overview.
Sourav Nandy
As AI infrastructure scales, gaining true visibility into GPU clusters has become a massive headache. Most monitoring dashboards give you raw, out-of-context metrics. You might see a GPU pinned at 100% utilization, but you have no idea what it is actually doing. We were tired of playing a guessing game to figure out if a node was crunching through a training job or serving user inference requests.
We built WorkloadTruth to eliminate this blind spot. It is designed to classify GPU workloads strictly as either inference or training based solely on telemetry data. By looking at hardware behavior signatures (rather than application code), you get the ground truth of what your GPUs are doing.
Initially, we were going to build this as a standalone monitoring script or a traditional DevOps dashboard widget. But while building the classification logic, we realized that the modern developer workflow is shifting towards AI agents. Instead of making users check yet another dashboard, we evolved the project into a Model Context Protocol (MCP) server.
By exposing these classification capabilities via MCP tools, you can now seamlessly connect WorkloadTruth to clients like Claude Desktop or Cursor. Now, you can just ask your AI assistant what your GPUs are doing, and it can natively read the telemetry to give you an answer.
Repo: https://github.com/RudrenduPaul/WorkloadTruth
MCP Servers:
https://mcpservers.org/servers/rudrendupaul/workloadtruth
https://glama.ai/mcp/servers/RudrenduPaul/workloadtruth
NPM: https://www.npmjs.com/package/workloadtruth-cli
PyPI: https://pypi.org/project/workloadtruth-cli
We’d love to hear your feedback, feature requests, or how you plan to integrate this into your own AI infrastructure.