What Is GPU CI? Running Pipelines on GPU Runners Explained

GPU CI is running continuous integration jobs on GPU-equipped runners, so machine learning training, inference tests, and CUDA builds can execute in the pipeline.

Most CI runs on CPU, which is fine for building and testing ordinary software. Machine learning is different: training and many model tests are dramatically faster on a GPU. GPU CI gives your pipeline access to that hardware so ML work can run automatically, not just on someone laptop.

What GPU CI is

GPU CI means provisioning CI runners that have one or more GPUs attached, so jobs can use frameworks like PyTorch or TensorFlow with hardware acceleration. It is what lets training, GPU inference tests, and CUDA-dependent builds happen inside an automated pipeline.

Why ML needs it

Deep learning training on CPU can be tens to hundreds of times slower than on GPU. Running these jobs without a GPU is often impractical. GPU CI also matters for testing that GPU code paths actually work, since CPU-only tests cannot exercise CUDA kernels.

The cost angle

GPU runners are expensive per minute, so the goal is to use them only where they help. A good pattern routes the heavy training or inference job to a GPU runner while everything else - linting, data validation, CPU tests - stays on cheap CPU runners.

GPU CI in practice

Select a GPU runner for just the job that needs it, keeping the rest of the pipeline on CPU.

Routing one job to a GPU runner

jobs:
  lint:  { runs-on: small }            # cheap CPU
  train:
    runs-on: gpu-large                  # GPU only here
    steps:
      - run: nvidia-smi
      - run: python train.py

Latchkey note

On Latchkey, GPU and large runners are available on demand so you pay for GPU minutes only on the jobs that need them. Caching large datasets, model weights, and CUDA-heavy dependencies between runs keeps expensive GPU jobs short, and auto-retry stops a transient data fetch from wasting a costly GPU run.

Key takeaways

GPU CI runs pipeline jobs on GPU-equipped runners so ML training, inference tests, and CUDA builds can execute.
Deep learning is far faster on GPU, and some code paths can only be tested with a real GPU.
Route only the heavy job to a GPU runner and cache big assets to keep expensive GPU minutes down.