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You can connect a GitHub Actions workflow to an Ollama model running on a Linux VPS, but “small” does not identify a reliable server size. The model and its context window determine much of the inference memory; the runner’s checkout, build and test work add their own load. Start with the model you intend to use, then measure it on representative pull requests before deciding whether the VPS is adequate.
How the workflow and Ollama fit together
Ollama and a GitHub Actions self-hosted runner are separate components: the runner executes the workflow, and a workflow step sends selected change data to Ollama and handles its response. Their official documentation describes the pieces, not a single ready-made code-review integration.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →- Choose a model and configuration. Pull the specific Ollama model and tag you plan to use. Record its variant or quantization when available, along with the context configuration, so later runs use the same setup.
- Run the workflow on a self-hosted runner. The runner may be on the Ollama VPS or on a separate worker. A single host is simpler, but inference and workflow jobs then share CPU, memory and disk, and the workflow execution environment shares the server’s security exposure.
- Send a bounded change to Ollama. Have the workflow select the relevant patch or files, enforce practical diff and context limits, and call the Ollama endpoint. Avoid sending an entire repository by default.
- Return the result for human review. The workflow can publish or otherwise surface the model’s response, but treat it as a suggestion—not a verified finding or a substitute for human review.
Ollama’s documented local API base is http://localhost:11434/api; it also supports OpenAI-compatible requests under http://localhost:11434/v1. Local requests do not require an API key. These local endpoints are distinct from Ollama cloud endpoints, which use a different base URL and require cloud authentication. See Ollama’s API introduction and Ollama’s quickstart.
How much RAM and storage does a VPS need?
There is no universal minimum in the documented figures. As one model-specific example, Ollama’s Quickstart, accessed October 4, 2026, lists a Gemma 4 E2B download of approximately 7.2 GB and recommends 8 GB of available VRAM or Mac unified memory for that example. Ollama cautions that a larger context window needs more memory. These numbers do not establish a minimum system-RAM figure or a complete VPS specification.
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Ollama says it may use system RAM when there is not enough VRAM, but responses may be slower. The documentation gives no speed estimate, so a CPU-only VPS may run a model without being fast enough for your workflow’s timing needs.
Budget disk for the model files as well as the operating system, repository checkout, workflow dependencies, logs and any build or test artifacts. For memory, account for the model’s runtime and context overhead, plus the runner and whatever the job does. A larger patch, longer context or additional concurrent jobs can change the load. Measure the actual configuration rather than treating the Gemma example as a general sizing rule.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Local inference or Ollama Cloud?
You can keep Ollama local to the VPS, or have the workflow use an Ollama cloud model. The right choice depends on where change data goes, how credentials are handled, model availability, service connectivity and measured response time. The official pages reviewed do not provide comparative prices or latency benchmarks.
| Choice | Data path and credentials | Infrastructure considerations |
|---|---|---|
| Local model on the VPS | Requests go to the local Ollama API; local requests do not require an API key. | You provide disk and compute resources for the model and its context. A GPU is not guaranteed by a VPS listing, and system-RAM fallback may be slower. |
| Ollama Cloud model | Requests use a cloud endpoint rather than the local API and require cloud authentication. | The workflow depends on external service connectivity. Keep credentials server-side and out of browser code and source control. The reviewed documentation does not establish comparative cost, latency or model availability for a particular workload. |
These are different data and operations paths, not interchangeable URLs. Decide what repository content may leave the host before enabling a hosted service.
What a GitHub self-hosted runner requires
GitHub says a self-hosted runner can be any machine able to run its runner application, communicate with GitHub and provide enough hardware for the planned workflows. Linux distributions and architectures supported by the runner are listed in GitHub’s self-hosted runner reference. Workflows that use Docker container actions or service containers also require Linux and Docker.
The same reference specifies outbound HTTPS on port 443 and at least 70 kilobits per second upload and download for runner communication. That is a communication floor, not a recommendation for model downloads or production throughput: pulling model files, checking out repositories and installing workflow dependencies can require substantially more bandwidth. The runner must also reach GitHub’s listed domains.
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Jobs without a matching online, idle runner remain queued and may fail after 24 hours in the queue. For autoscaling, GitHub recommends ephemeral runners; each accepts one job, leaving a clean environment after that job. A persistent runner on one personal VPS is simpler, but it has a different isolation profile and needs more care around which jobs it accepts.
Should the VPS have a GPU?
Not necessarily. Ollama can use system RAM when VRAM is insufficient, although the Quickstart warns that responses may be slower. Whether a GPU improves your actual review workflow enough to justify its cost or availability constraints is a workload question, not something the documentation’s compatibility list answers.
For NVIDIA, Ollama’s compatibility guidance specifies compute capability 5.0 or newer with driver 550 or newer; devices with compute capability 5.0–6.2 need driver 570 or newer. AMD support depends on supported cards and the ROCm driver stack. Check the current Ollama GPU compatibility guidance against the exact hardware, driver and runtime offered by a provider. A VPS plan mentioning a GPU does not by itself establish that Ollama can use it.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsHow to test whether your chosen VPS is adequate
- Fix the configuration. Record the model tag, variant or quantization, context setting, prompt and diff-selection policy. Changing any of them can change the resource profile or result.
- Use representative pull requests. Try changes typical of the repository, including larger diffs that fit your intended limits. Include the runner’s actual checkout and build or test steps.
- Measure the whole job. Track peak memory, inference latency and timeouts while Ollama and the runner are active together. Check whether jobs overlap or queue under the concurrency you expect.
- Judge usefulness, not just completion. Review whether the output identifies actionable issues without overwhelming reviewers with irrelevant comments. The cited official documentation does not publish a code-review accuracy rate or guarantee defect detection.
- Adjust before increasing capacity. Reduce the diff or context scope, limit concurrent jobs, or use a different model or host if measurements show the workload exceeds the available resources or time budget.
This test is the practical way to answer “Can my small VPS do it?” for your repository. No documented model example or runner minimum can guarantee useful, timely reviews across different patches and workflows.
Keep pull-request execution within a trust boundary
A review workflow may process code and instructions supplied through a pull request, so decide which changes are allowed to trigger it and what the job can access. Avoid giving a job handling untrusted changes unnecessary secrets or broad access to the persistent host. Keep cloud credentials, if used, server-side and out of source control. GitHub’s ephemeral-runner guidance is a useful reference for cleaner per-job environments, especially when scaling beyond a single controlled runner.
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