Windows Task Manager can report 15 GB of GPU memory in use while leaving the allocation unexplained. Model weights, a large-language model’s key-value (KV) cache, browser compositor surfaces, and GPU-accessible system RAM appear in separate counters or remain difficult to connect. Scott Hanselman has released the open-source gputaskman repository to gather those signals in one terminal interface.

The MIT-licensed app is written in Python with Textual and targets NVIDIA GPUs on Windows. It is available as a portable Windows ZIP or a source checkout. Its main use is diagnosing local AI workloads that exceed VRAM, use unexpectedly large KV caches, or share the GPU with other applications.

One screen, three layers

The terminal interface organizes its telemetry into device, process, and model views:

gputaskman showing compositor activity and per-process VRAM use on an RTX 4080 Super
The process view separates local GPU memory from GPU-accessible system memory.

The live collector also supports automation. The --once option prints one text snapshot, while --json emits structured data from the same collector used by the interface. Both modes can feed diagnostic scripts, dashboards, and CI logs.