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How to run GLM-5.3-Flash on a rented Google Cloud TPU VM with Kaggle Tpu Lab

Use the gcp/ guide and scripts to serve GLM-5.3-Flash without the Kaggle queue or time cap.

Who it is forA team or developer who needs GLM-5.3-Flash running on demand and can rent a Google Cloud TPU VM.

The problemKaggle sessions are capped at a little under nine hours and depend on a free quota, which is not always enough.

The outcomeGLM-5.3-Flash runs on a Google Cloud TPU VM with no queue and no nine-hour cap, using the repo's environment, dataset and run scripts.

What you need first

  • A Kaggle account with TPU access, verified under Settings (phone number, and for some accounts a Persona identity check)
  • Kaggle's free TPU quota, around 20 TPU hours a week
  • For the terminal route: Python 3.9+ and the Kaggle CLI

Steps

  1. Confirm you have access to a Google Cloud TPU VM of type v5litepod-8 or v6e-8.
  2. git clone https://github.com/ARahim3/kaggle-tpu-lab
  3. cd kaggle-tpu-lab/gcp (inferred, check the README)
  4. Follow the guide in gcp/ and run the environment script, then the datasets script.
  5. Run the run script from gcp/ and wait for the server to start.

Prompt for your agent

In the ARahim3/kaggle-tpu-lab repo, follow the gcp/ guide to set up a v5litepod-8 or v6e-8 TPU VM, run the environment, datasets and run scripts, and serve GLM-5.3-Flash. Expected output: a running server on the TPU VM. Verify the server responds to one test request before reporting success.

Caveat. The README does not give rental prices, so check current Google Cloud TPU VM costs before starting.

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