#colab

Google AI Ultra includes monthly Colab compute that, at A100 rates, runs for over 200 hours, which is a good deal for running open-source models. Compares the official Colab CLI and the remote Colab MCP: the CLI suits complex deployments, while MCP lets you turn GPUs on and off from within a chat at any time. Includes a quota table, hands-on steps, cost-saving principles and FAQs.

Use when a user wants an agent to start a GPU or TPU on Google Colab, run code, deploy or fine-tune open-source models, fetch results, and release the machine. Combines the official google-colab-cli (Apache-2.0) and the Colab remote MCP into one workflow: the CLI handles file transfer, SSH, Drive, and one-off jobs, while MCP handles lightweight start/stop from a chat client. Enforces releasing runtimes after use, asking before expensive GPUs, and never paying on the user's behalf. Use case: Let an agent start a GPU on Colab for you, run open-source models, fetch the results, and shut down automatically. Use the CLI from a local terminal, or the remote MCP from a chat client.

A breakdown by use of what Google AI Ultra bundles: Colab compute, monthly Cloud credits, Flow image and video quotas, Antigravity coding quotas, 30TB of storage and various memberships, plus how the quotas are calculated separately. The list comes from materials provided by users; the official plan page is the reference.