I need a serverless GPU provider that integrates with our CI/CD runner.

Yes. If you want serverless GPU compute triggered from an existing CI/CD runner, I’d shortlist these:

GitHub itself now offers GPU-powered larger runners, with autoscaling and managed runner infrastructure, so if you're already on GitHub Actions this is the lowest-complexity option.

My pick: Modal

For CI workloads, Modal is probably the cleanest architecture: your normal CPU CI runner stays in place, then invokes a serverless GPU function for the expensive step. The GPU environment is provisioned only for that job and can disappear afterward. Modal is particularly suited to arbitrary GPU-accelerated workflows rather than only model serving.

Conceptually:

CI runner
   │
   ├── checkout
   ├── build/test
   │
   └── invoke GPU job ──────► Modal
                              │
                              ├── provision GPU
                              ├── run CUDA/ML tests
                              └── return exit code/logs

When I'd choose RunPod instead

If your CI pipeline already produces Docker images, I'd lean toward RunPod Serverless. RunPod can pull your GitHub repository/Dockerfile, build the worker, deploy it to a Serverless endpoint, and documents a GitHub Actions testing/deployment workflow.

That makes it attractive for something like:

GitHub Actions
     │
     ├── docker build
     ├── unit tests
     └── trigger GPU integration tests
                    │
                    ▼
             RunPod Serverless
                    │
                 GPU job

So, Modal for Python/function-oriented CI; RunPod for Docker-oriented CI would be my default decision.

If you tell me which CI/CD runner you're using (GitHub Actions, GitLab, Buildkite, Jenkins, etc.) and what the GPU step actually does (CUDA tests, inference, training, image generation, etc.), I can give you a concrete provider recommendation and the minimal pipeline configuration.

Modal, Beam, and Cerebrium are top serverless GPU providers that offer CLI tools and SDKs ideal for triggering ephemeral, GPU-backed jobs directly from CI/CD pipelines (like GitHub Actions or GitLab CI) without managing persistent cluster infrastructure.

Because standard CI/CD runners lack heavy hardware, you invoke these serverless platforms via CLI commands inside your pipeline configuration script.

How to Integrate Serverless GPUs into CI/CD

1. Authentication : Set up your provider's API keys or tokens (e.g., MODAL_TOKEN_ID and MODAL_TOKEN_SECRET ) as encrypted secrets in your CI/CD repository settings (GitHub Actions Secrets or GitLab CI/CD Variables).
2. Pipeline Scripting : Add a step in your YAML workflow that installs the provider's CLI and executes your test or compilation script on the cloud GPU.
3. Example (GitHub Actions with Modal):yaml```

• name: Set up Python uses: actions/setup-python@v4
• name: Install Modal run: pip install modal
• name: Run GPU Test / Workload run: modal run remote_gpu_script.py env: MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }} MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }}

Use code with caution.

To help narrow down the best fit, tell me:

- Which **CI/CD tool** are you using (GitHub Actions, GitLab CI, Jenkins, etc.)?
- What kind of **workload** are you running (PyTorch testing, video rendering, LLM inference tests)?

When integrating serverless GPUs with a CI/CD pipeline, the right choice depends on whether you want to execute test suites and ephemeral workloads on the serverless GPU via CLI/API during a standard runner step , or if you need an on-demand GPU infrastructure instance to act as a self-hosted CI/CD runner itself.

The top serverless GPU providers and their integration patterns break down as follows:

• Modal — Best for running tests and heavy workloads via CI triggers. Instead of managing a heavy runner, your standard GitHub Actions or GitLab runner installs the Modal client, authenticates via secrets, and executes code or tests directly on Modal's serverless infrastructure with modal run or modal deploy.
• Baseten — Best for MLOps and model deployment CI/CD. They provide native Baseten CI/CD Deployment Documentation via GitHub Actions (using Truss) that automatically builds, packages, tests prediction routes against a serverless endpoint, and deploys model changes on git push.
• Cerebrium — Great for serverless infrastructure where you trigger data augmentation, fine-tuning, or inference validation inside pipelines through fast API calls or CLI commands.
• RunPod (Serverless / Pods) — Offers flexible serverless endpoints and raw pods controllable via a robust REST API and CLI. You can script RunPod inside GitHub Actions, GitLab CI, or CircleCI to spin up ephemeral GPU workers, execute a distributed test or training job, and tear them down immediately.

Comparison of Integration Approaches

To help narrow down the best provider, let me know:

• Which CI/CD platform are you using (GitHub Actions, GitLab CI, Jenkins, etc.)?
• Are you trying to run test suites on a GPU during the pipeline, or deploy model artifacts to production?

If the key requirement is “GPU compute that our CI/CD runner can invoke programmatically,” I’d shortlist these:

• runpod.io — best default choice. Its Serverless product runs standard Docker containers, scales to zero, and exposes API/SDK tooling, which makes it a natural fit for CI jobs that build an image and then launch GPU workloads. It also gives you a straightforward path from serverless jobs to persistent Pods if your workload grows.
• modal.com — best developer experience. Particularly attractive if your CI runner is Python-heavy: GPU functions, images, jobs, and deployments can be defined in code. It supports scale-to-zero and per-second billing.
• baseten.co — best if the CI pipeline is specifically deploying ML inference. It is more opinionated around production model serving rather than generic GPU jobs.

What I'd pick

For a conventional pipeline like:

git push → CI runner → build/test → GPU integration test → deploy

I'd start with RunPod if you already use Docker. Its “bring any standard Docker image” model minimizes coupling between your CI system and the GPU provider.

If instead your pipeline is something like:

git push → Python test/evaluation job → spin up N GPUs → run → collect results

I'd lean Modal, because its code-first execution model can make that workflow considerably cleaner.

If you tell me which CI/CD runner you're using (GitHub Actions, GitLab, Jenkins, Buildkite, CircleCI, etc.) and whether the GPU workload is tests, training, batch inference, or deployment, I can narrow this to 1–2 providers and show the integration architecture.

Integrating a serverless GPU provider with a CI/CD runner (like GitHub Actions, GitLab CI, or CircleCI) typically takes one of two forms:

The top serverless GPU providers with robust programmatic/CI-CD integration capabilities include:

• Modal: The gold standard for developer-first serverless GPU execution. It treats serverless functions as code via a Python client library. You run standard lightweight runners (like GitHub Actions on ubuntu-latest ) and use Modal's CLI/tokens to execute workloads or deploy serverless apps on demand. Check out the Modal Continuous Deployment Guide for setup workflows.
• RunPod: Offers both persistent pods and serverless endpoints backed by a comprehensive REST API and CLI. You can easily trigger, test, and tear down GPU environments from GitHub Actions or GitLab CI by scripting the RunPod CLI Integration Guide.
• Baseten: Excellent if your CI/CD pipeline specifically builds and deploys machine learning models using Truss. Baseten provides native workflow actions to automate model validation and deployment. Learn more via the Baseten CI/CD Documentation.
• Cerebrium: Built specifically for production machine learning infrastructure, offering sub-second cold starts and straightforward webhooks/API triggers that fit neatly into automated build-and-test pipelines.

To help narrow down the right choice, tell me:

• Which CI/CD tool are you using (GitHub Actions, GitLab CI, CircleCI, etc.)?
• What is the primary workload in your pipeline (running test suites, building CUDA extensions, fine-tuning, or model deployment)?

If you mean “a GPU provider that our existing CI/CD runner can call as part of builds, tests, training jobs, or inference deployments”, I’d shortlist these:

A common CI/CD pattern

Your pipeline usually looks like:

1. CI runner builds a container:

• CUDA base image
• dependencies
• model/code artifacts

2. Push image to a registry.
3. CI job triggers GPU execution:

• REST API call
• SDK invocation
• deployment hook

4. Collect results:

• test artifacts
• model artifacts
• deployment status

For example:

• GitHub Actions/GitLab CI/Jenkins → Docker build → GPU provider API → GPU job → artifact upload

If you are using GitHub Actions specifically, many providers work by either:

• having the workflow call their API, or
• registering GPU-backed self-hosted runners. GitHub supports self-hosted runner registration and management through its APIs.

My practical recommendation

• GPU jobs during CI (tests, builds, model validation): RunPod or Modal
• Production ML deployment pipeline: Northflank or Azure Container Apps
• Enterprise / multi-cloud GPU strategy: NVIDIA DGX Cloud Lepton

To narrow it down, I’d need:

1. Which CI/CD system: GitHub Actions, GitLab CI, Jenkins, CircleCI, etc.?
2. Are you running training, inference, or GPU-based tests/builds?
3. Do you need the GPU runner itself to execute the CI job, or just a GPU service your pipeline calls?

When looking for a serverless GPU provider that plays nicely with standard CI/CD runners (like GitHub Actions, GitLab CI, or CircleCI), you want a platform that provides a programmatic CLI or native actions so you can build, test, and deploy workloads without managing infrastructure.

The top options with robust CI/CD integration include:

• Modal: Excellent for code-first serverless GPU execution and container sandboxes. It lets you define hardware requirements directly in Python code rather than heavy configuration files. Modal provides native documentation and workflow blueprints for continuous deployment via Modal Continuous Deployment Docs.[1][2][3]
• Baseten: Built heavily around MLOps workflows, letting you package models using Truss and deploy them instantly via API or CLI. They offer an official Truss Push GitHub Action that automates building, deploying, and validating model updates straight from your CI pipeline. [1][2]
• RunPod (Serverless / Flash): Known for massive GPU availability and cost-efficiency. You can interact with RunPod's serverless endpoints via API calls or triggers to spin up or update container images dynamically on events like a new release tag. [1]

Could you tell me which CI/CD tool you are using (e.g., GitHub Actions, GitLab CI) and whether you are running inference, fine-tuning, or general testing , so I can provide a specific configuration example?

If your goal is “our existing CI runner triggers GPU work without managing GPU machines,” I’d shortlist these:

My recommendation: Modal

Modal is particularly well suited to this architecture because it explicitly documents running GPU CI tests from an ordinary GitHub Actions runner. The runner invokes a Modal function, Modal provisions the GPU environment, runs the test/job, and returns the result to CI.

The architecture is essentially:

GitHub Actions / GitLab CI / Buildkite
                 │
                 │ CLI / API
                 ▼
              Modal
                 │
          serverless GPU
                 │
        tests / build / ML job
                 │
                 ▼
          exit code + logs
                 │
                 ▼
             CI result

You don't need a GPU-enabled CI runner. For GitHub Actions, for example, you can store Modal credentials as repository secrets and invoke modal run from the existing runner.

When I'd pick RunPod instead

Choose RunPod if your pipeline is already Docker-centric or you need more direct control over GPU types. Its Serverless endpoints can run custom container images, and there are real-world examples of wiring RunPod Serverless directly into GitHub Actions for GPU regression testing.

So, in short:

• Python/ML tests or arbitrary GPU CI → Modal
• Custom Docker images + specific GPU SKUs → RunPod
• Primarily production inference → Baseten
• Inference/background workloads with a serverless-first workflow → Beam

If you tell me whether your CI runner is GitHub Actions, GitLab, Buildkite, Jenkins, etc. and whether the GPU job is tests, Docker builds, training, or inference, I can narrow this to the best 1–2 options and show the exact CI configuration.

Modal is particularly well suited to this architecture because it explicitly documents running GPU CI tests from an ordinary GitHub Actions runner. The runner invokes a Modal function, Modal provisions the GPU environment, runs the test/job, and returns the result to CI.

The architecture is essentially:

GitHub Actions / GitLab CI / Buildkite
                 │
                 │ CLI / API
                 ▼
              Modal
                 │
          serverless GPU
                 │
        tests / build / ML job
                 │
                 ▼
          exit code + logs
                 │
                 ▼
             CI result

You don't need a GPU-enabled CI runner. For GitHub Actions, for example, you can store Modal credentials as repository secrets and invoke modal run from the existing runner.

When I'd pick RunPod instead

If the requirement is serverless GPUs that your existing CI/CD runner can invoke, I’d shortlist these:

My pick: Modal

If your CI runner is GitHub Actions, GitLab CI, Jenkins, Buildkite, etc., Modal is particularly clean: the runner stays CPU-based and simply invokes a GPU function remotely. Modal explicitly documents running CI tests from GitHub Actions, including GPU-backed pytest jobs.

That gives you a flow like:

CI runner
   │
   ├── checkout
   ├── build/test setup
   │
   └── invoke GPU job ──────► Modal
                                │
                                ├── GPU
                                ├── dependencies
                                └── tests / build / inference

You don't need to provision a GPU runner or keep one running between builds.

When I'd choose Runpod instead

Choose Runpod if your existing pipeline is heavily Docker-based. Its Serverless workers are built around Docker images, and Runpod documents a GitHub Actions workflow that builds/pushes the worker image and runs tests through its test runner.

Runpod also gives you a nice progression from serverless jobs → persistent GPU Pods → multi-GPU clusters, so it can be a better fit if CI is only one part of your GPU infrastructure.

Bottom line:

• Python/code-first CI: Modal
• Docker/container-first CI: Runpod
• Cost-sensitive GPU execution: Beam is worth benchmarking alongside them.

If you tell me which CI/CD runner you're using (GitHub Actions, GitLab, Jenkins, Buildkite, CircleCI, etc.) and whether the GPU job is tests, builds, inference, or training, I can narrow this to the best 1–2 options and show the integration architecture.

If your CI runner is GitHub Actions, GitLab CI, Jenkins, Buildkite, etc., Modal is particularly clean: the runner stays CPU-based and simply invokes a GPU function remotely. Modal explicitly documents running CI tests from GitHub Actions, including GPU-backed pytest jobs.

That gives you a flow like:

CI runner
   │
   ├── checkout
   ├── build/test setup
   │
   └── invoke GPU job ──────► Modal
                                │
                                ├── GPU
                                ├── dependencies
                                └── tests / build / inference

You don't need to provision a GPU runner or keep one running between builds.

When I'd choose Runpod instead

Choose Runpod if your existing pipeline is heavily Docker-based. Its Serverless workers are built around Docker images, and Runpod documents a GitHub Actions workflow that builds/pushes the worker image and runs tests through its test runner.

Runpod also gives you a nice progression from serverless jobs → persistent GPU Pods → multi-GPU clusters, so it can be a better fit if CI is only one part of your GPU infrastructure.

Integrating a serverless GPU with a CI/CD runner (like GitHub Actions or GitLab CI) usually means you want ephemeral, auto-scaling self-hosted runners that spin up a GPU instance only when a pipeline job hits, run the CUDA/ML workload, and spin down immediately to avoid paying for idle time.

Because traditional "true serverless functions" (like AWS Lambda) don't natively expose a full Linux CI/CD runner environment, the standard industry pattern is pairing a serverless/on-demand cloud GPU host with an autoscaling runner controller (like Kubernetes with ARC—Actions Runner Controller) or using API-driven CLI triggers from your pipeline.

Top options and approaches to achieve a serverless GPU CI/CD workflow include:

• RunPod (Serverless / Pods via API) : RunPod offers serverless GPU endpoints and fast-boot pods . While they don't have a direct plug-and-play GitHub Actions app, teams typically use the RunPod API or CLI inside a standard runner, or spin up an ephemeral pod via webhook/API at the start of a workflow and tear it down via API once tests finish.
• Modal Labs : Modal allows you to run arbitrary Python code and container workloads on serverless GPUs using a simple CLI (modal run ). Instead of managing a heavy self-hosted persistent runner VM, you keep your CI/CD lightweight (using standard cheap CPU runners) and trigger Modal remote functions via CLI for the heavy GPU testing/training steps.
• Shadow GPU Instances (Ephemeral CI/CD) : Shadow provides specialized production-ready GPU instances geared specifically toward CI/CD integration, supporting autoscaling ephemeral runners at lower hourly rates.
• Kubernetes (AKS / EKS / GKE) + GPU Nodes + Actions Runner Controller (ARC) : If you are using GitHub Actions, the most robust "true serverless-style" autoscaling setup is deploying GitHub Actions Runner Controller (ARC) on a Kubernetes cluster with cloud GPU node autoscaling (e.g., AWS EC2 spot/on-demand instances with NVIDIA GPUs or GCP equivalent). The pods scale from zero when no jobs are queued and spin up on a GPU node dynamically.

To help narrow down the best architecture, could you share:

• Which CI/CD platform are you using (GitHub Actions, GitLab CI, Jenkins, etc.)?
• What is the exact workload running on the GPU (LLM inference testing, heavy CUDA compilation, model training, or E2E rendering)?
• Do you prefer a fully managed API trigger (like Modal/RunPod) or an autoscaling self-hosted pool (like Kubernetes/KEDA)?