tokenwatt-check

Is the GPU you rented performing like it should?

GPU listings tell you the model — "H100 SXM, verified, 99.5% reliability" — not the card you get. When we benchmarked four rented H100s, two had their power limit lowered by the host and one ran at 787 MHz instead of ~1,900 under load: every token took 2.9× longer. Nothing on the listing showed it.

tokenwatt-check finds that in about two minutes:

pip install tokenwatt-check torch    # torch is usually already there on GPU images
tokenwatt-check

  OVERALL: ❌ FAIL

  ⚠️  power limit            GPU power limit is 500 W of a possible 700 W (71%).
  ❌ clocks under load      SM clock during the matmul averaged 560 MHz — 41% of a healthy H100 (1360 MHz).
                            → Far below normal. Possible causes: aggressive power cap, poor cooling or
                              power delivery, a degraded card. Ask for another machine.
  ❌ BF16 matmul            280 TFLOPS — 41% of the healthy H100 reference (680 TFLOPS).
  ❌ memory copy bandwidth  2.2 TB/s — 73% of the healthy H100 reference (3.0 TB/s).
  ✅ PCIe link              Gen5 x16.
  ✅ disk                   Sequential write 1,616 MB/s.
  ✅ download               2,100 Mbit/s from Hugging Face, one stream (~6.3 min per 100 GB of weights).

(Illustrative output modeled on a real under-performing node — see src/tokenwatt_check/demo.py. Try tokenwatt-check --demo throttled-h100, capped-h100 or healthy-h100 without a GPU.)

What it checks

Results: PASS, WARN or FAIL per check with a reason and what to do, plus a JSON file with every reading (--json path) and an optional Markdown report (--md path). Exit code 2 on FAIL, for scripts.

Options

tokenwatt-check --gpu 1            # another GPU
tokenwatt-check --seconds 60       # longer load test
tokenwatt-check --no-load          # inventory, disk and network only (no PyTorch needed)
tokenwatt-check --no-net --no-disk
tokenwatt-check --share --provider runpod   # add an anonymized copy to the public node statistics
tokenwatt-check --watch            # keep watching during your workload (see below)

Watch mode: catch changes during the rental

A check at the start can't see a host lowering the power limit halfway through your rental, a card that starts throttling when the room heats up, or a PCIe link that renegotiates down. Leave watch mode running next to your workload:

tokenwatt-check --watch                       # every 60 s, all GPUs, until Ctrl-C
tokenwatt-check --watch --interval 30 --hours 24 --log node-a.jsonl
tokenwatt-check --watch --on-event 'curl -s -X POST -d "$TWC_MESSAGE" https://hooks.example.com/gpu'

watching every 60 s · log tokenwatt-watch-20261010-141500.jsonl · Ctrl-C to stop and summarize
  GPU 0 NVIDIA H100 80GB HBM3: power limit 700.0 W, max clock 1980 MHz, PCIe Gen5 x16
[16:42:07] ⚠️  GPU 0: power limit changed from 700 W to 525 W (75% of the 700 W at start) — throughput will drop
[16:44:07] ❌ GPU 0: SM clock under load is 787 MHz, 40% of max (1980 MHz) — far below a healthy card; your work is running much slower

It flags, the moment they happen: power-limit changes, hardware or thermal throttling starting and ending, the SM clock under load falling below half of max for three samples in a row (healthy cards stay around two-thirds even at their power limit), temperature at 87 °C or more, PCIe width or generation dropping, and new uncorrected ECC errors. Every sample goes to a JSONL log; when you stop it you get a summary per GPU (lowest power limit, share of time under load and throttled) and an overall PASS / WARN / FAIL. Watch mode only reads nvidia-smi, so it adds no load.

Reference values

Compute, bandwidth and clock-under-load are compared with a healthy card of the same model (src/tokenwatt_check/verdict.py):

A dense matmul pulls a healthy card to its power limit, so its clock sits well below the maximum — a healthy H100 holds about 69% of max clock in this test. That's why the clock check compares against a healthy card rather than the maximum. If a healthy card scores low on your machine, please open an issue with your JSON so we can improve the references.

Privacy

Everything runs locally. Nothing is sent anywhere except the 20-second download test from huggingface.co — unless you add --share. Then one anonymized report goes to TokenWatt: GPU model, driver, limits and measurements, CPU count and RAM size, and the provider name if you give one. Hostnames, GPU UUIDs, paths and your IP address are not kept. Shared reports are published only as aggregate statistics per GPU model, which is how the healthy-card references get better for everyone. Details: privacy policy

Supported

NVIDIA GPUs with nvidia-smi (H100, H200, B200, A100, L40S, RTX 4090/5090 and others). AMD Instinct support is planned.

About

Built by TokenWatt, which publishes vendor-neutral measurements of LLM inference — SLO goodput, tokens per joule and cost per million tokens — on NVIDIA and AMD. MIT licensed.