Do you need a GPU for AI video? Only to run the model

You need a GPU for AI only if you run the model yourself. Running an image or video model on your own computer or server needs a GPU with enough memory for that model; calling a hosted AI video API does not, because your code only sends an HTTPS request and downloads the finished file.

The hardware facts below come from the Hugging Face Diffusers memory guide, read 2026-09-28. The API facts come from Sume's video generation docs.

Why does AI need a GPU?

A modern image or video model is billions of numbers, and generating an image or a frame means multiplying huge grids of them over and over. A GPU runs many of those multiplications in parallel, which is why AI models are run on GPUs rather than on an ordinary processor.

Memory is the real limit. The Diffusers guide says "modern diffusion models like Flux and Wan have billions of parameters that take up a lot of memory on your hardware for inference", and that "common GPUs often don't have sufficient memory". Its workarounds are more than one GPU, or moving parts of the model to the CPU (Diffusers).

Can my laptop run AI video?

It can call a video API, and that needs nothing more than a network connection. Running the model locally is another matter. The same guide says CPU offloading "dramatically reduces memory usage, but it is also extremely slow" and "can often be impractical due to how slow it is" (Diffusers). On a laptop whose GPU has too little memory for the model, offloading is the fallback the guide describes.

What does calling a video API look like instead?

Four HTTP steps: submit to POST /v1/videos, receive a job id and a polling URL at once, poll GET /v1/videos/{jobId} until the status is completed, then download from the content URL. Sume's docs say generation "typically takes 30 seconds to several minutes depending on the model and parameters" (Video generation).

The content URL needs your API key and, per the API reference, redirects to the generated video, so use curl -L; Download a generated video covers that step. Jobs are reserved on submit at provider list × 1.25, so the bill is per output, not per GPU hour.

curl -X POST "https://api.sume.com/v1/videos" \
  -H "Authorization: Bearer $SUME_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "seedance-2",
    "prompt": "A golden retriever playing fetch on a sunny beach",
    "resolution": "720p"
  }'
# {"id": "job_123", "polling_url": "https://api.sume.com/v1/videos/job_123", "status": "pending", ...}

Can I self-host an AI video generator?

Yes, if you have open model weights and a GPU that fits them; the model's own page is the place to check its memory needs. Sume is not a source of weights: in current code every model in its video catalog reports hugging_face_id: null, and there is nothing to download and run yourself.

A middle path is a serverless GPU host that runs open models for you and bills compute time. What is serverless inference? explains how that is billed, next to per-output APIs like Sume's. For how the models themselves produce video, see How does AI video generation work?.