MiniMax H3 can run on a Google Colab GPU while your local computer prepares reference pictures, sends jobs, and downloads the resulting MP4. A local command line does not mean local inference: the GPU, model weights, and generation remain in the remote runtime.
This guide examines the community Colab runner, its bundled notebook, and runner source, checked October 16, 2026. It is a documentation-based workflow, not a generation we performed. H3Vid does not supply the Colab GPU or guarantee its allocation.
Choose a route and inspect its dependencies
The bundled notebook has two modes. Its browser-oriented default uses FL2VA with a first image. The local launcher switches to Ref2VA for one to nine semantic reference pictures. Those are different conditioning paths; sending several files to a first-frame graph does not make them ordered character references.
The notebook selects compatible INT8, FP8, or BF16 weights based on CUDA and GPU capability and pairs the reference model with its own acceleration adapter. It also needs a text encoder and both video and audio VAEs. Its guidance asks for substantial free disk space: roughly sixty GiB, or eighty GiB for the BF16 reference fallback. Check the actual runtime before downloading.
The MiniMax model card and its license remain relevant even when a community notebook automates installation. Colab access does not change model-use conditions. Verify eligibility for your intended use and deployment region against the current upstream terms before allocating compute.
Prepare the local command line
The runner requires Python 3.11 or newer, while the documented current Colab CLI installation uses Python 3.12. These requirements refer to different programs. An existing local Python environment for the runner need not be replaced simply to install the CLI as an isolated tool.
Clone the community repository and review its README and notebook. The documented installation uses uv to install Python 3.12 and google-colab-cli, then checks colab version. Run the CLI's OAuth usage command interactively for initial authentication and inspect the account's compute balance. The runner defaults to OAuth2 and has an explicit alternative for existing ADC setups.
The repository's skill installer is optional when using its shell launcher directly. It copies the files needed by that named skill; it does not perform GPU inference during installation. If you use the installed skill, keep its runner and notebook together so they remain compatible.
Define reference order before writing the prompt
Choose one or two clear images first. Their argument order becomes Picture 1, Picture 2, and so on; the runner validates that the prompt does not reference a picture number you did not upload. Those labels identify visual content, not timestamps in the final clip.
Write a UTF-8 prompt file that names each subject and gives one manageable action. For example, identify the traveler in Picture 1 and the companion in Picture 2, then describe them walking through an autumn market in one continuous shot. State the camera movement and desired sound. This is an original planning example, not a tested result.
When writing the actual prompt, follow the official Ref2VA guide. It provides the reference syntax and prompt structure. Keep the image order unchanged when revising words so you can interpret the next result.
Submit one short job
From the reviewed community checkout, the launcher accepts repeated image flags, a prompt path, and an output path:
H3_DURATION_SECONDS=5 ./run_colab_inference.sh \
--image /absolute/path/traveler.png \
--image /absolute/path/companion.jpg \
--prompt /absolute/path/prompt.txt \
--output /absolute/path/market.mp4
These are example paths to replace, not files supplied by H3Vid. The launcher requests an A100 high-memory runtime by default. Availability depends on the account, balance, plan, and current capacity; a free T4 is not an equivalent promise. The notebook recommends five to fifteen seconds even though its accepted boundary starts at four.
Requested seconds can also differ from encoded duration because H3 aligns frame geometry. Inspect the actual media rather than assuming a five-second request is exactly five seconds. Record the selected checkpoint variant and GPU with the result so later timing comparisons have context.
Batch without losing completed work
For several clips, create the documented job manifest and use scripts/runner.py batch. Each job can supply ordered images, a prompt file or inline prompt, duration, and output name. A single session avoids repeatedly allocating a GPU and loading the same models.
The runner records progress and preserves completed outputs if a later job fails. Sessions it creates are stopped in cleanup, including error paths; reusing a named existing session has different ownership, so use the explicit stop-on-complete option when appropriate. Inspect progress and remote state before rerunning a timed-out batch, since an uncertain local outcome does not prove the remote kernel stopped.
Check that the downloaded MP4 exists and contains the expected video and audio. The optional ffprobe check can verify streams; watching and listening verifies the actual scene. Download your selected files and logs before ending the runtime, then confirm no active assignment remains through the CLI.
A community report from October 12 describes one A100 setup and generation session. Its timings are one user's evidence, not a service-level expectation or proof that this separate runner has identical performance. For a hosted creative task without managing a Colab runtime, see H3 Ref2VA. For desktop preparation, use the ComfyUI setup guide.