You can run an open-weight AI model in a private cloud or on your own premises by selecting a model that fits your data and latency needs, sizing infrastructure for its actual workload, choosing an inference runtime, and securing and operating the service. For OpenAI’s gpt-oss models, that means running them on infrastructure you control or through a hosting provider—not calling them through ChatGPT or the OpenAI API.
What “open-weight” deployment means
Open-weight means the model weights are publicly available; it does not mean every component used to serve the model is open source. OpenAI says gpt-oss weights are licensed under Apache 2.0 and subject to its usage policy, while related infrastructure or tools can have different ownership or licensing. Check the terms for the exact model and every component you plan to use. OpenAI’s gpt-oss overview also says these models are not served through the OpenAI API and are not available in ChatGPT.
Self-hosting changes who operates the inference service. You provide or arrange the compute, storage, runtime, security controls, and ongoing maintenance. OpenAI says it does not receive data sent to a self-hosted gpt-oss model unless a user shares it or uses a managed hosting partner. That statement concerns OpenAI; it does not establish the data-handling practices of a separate cloud or hosting provider.
Choose between private cloud and on-premises
Both approaches can keep a model-serving environment within a controlled boundary, but they differ in who provides and operates the underlying infrastructure. “Private cloud” does not by itself guarantee a particular data location or level of isolation: those depend on the provider’s design and your configuration.
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| Consideration | Private cloud | On-premises |
|---|---|---|
| Infrastructure | GPU capacity may be provider-managed within an isolated environment, depending on the service. | Your organization sources, powers, cools, secures, and operates the hardware. |
| Residency and access | Verify physical location, provider access, isolation controls, and outbound data paths with the provider. | You control the physical site, but must still manage network access, backups, and any external connections. |
| Operational responsibility | Responsibility is shared according to the service boundary; establish who patches and monitors each layer. | Your team owns more of the hardware and infrastructure lifecycle. |
| Cost basis | Evaluate hosting and support charges alongside utilization and engineering effort. | Include hardware, storage, power, cooling, engineering, and maintenance. |
There is no universal cost winner. OpenAI notes that cost depends on workload and operating approach. Compare options using your expected concurrency, utilization, GPU availability, network topology, support needs, and data-residency requirements rather than assuming self-hosting is automatically cheaper.
Select the model and confirm its terms
Define the application before choosing a model: what data it will handle, the quality it needs to achieve, acceptable response time, prompt and context lengths, and expected concurrent requests. Then review the exact model card, license, usage terms, hardware guidance, and runtime compatibility.
For OpenAI gpt-oss
OpenAI describes gpt-oss-120b and gpt-oss-20b as open-weight reasoning models, and also documents safeguard variants for safety-classification and related trust-and-safety workflows. The safeguard variants are not interchangeable with a general-purpose selection decision; match the model’s intended use to your application. OpenAI’s model information gives these variant-specific figures:
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- gpt-oss-safeguard-120b: 117 billion parameters, approximately 5.1 billion active; designed to fit on one 80 GB GPU, with NVIDIA H100 given as an example.
- gpt-oss-safeguard-20b: 21 billion parameters, approximately 3.6 billion active; described as a lower-latency option or one for more constrained environments.
Those sizing descriptions apply to the named safeguard models. They are not requirements for every gpt-oss variant or every open-weight model, nor do they replace testing against your own context lengths and traffic.
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Size compute for the workload, not just the parameter count
Estimate memory needs for the chosen model, then leave room for runtime overhead, concurrent requests, the KV cache, and supporting services. Test representative prompt and output lengths under expected traffic; a model that loads successfully may still fail your latency or concurrency targets.
- Check GPU memory and the specific model’s hardware compatibility.
- Include context length and concurrent sessions in capacity planning because they affect serving resources.
- For multi-GPU or multi-node configurations, verify that the hardware topology and interconnect meet the runtime’s requirements.
- Measure utilization and latency under the same workload you expect in production before committing to a capacity plan.
The 80 GB GPU example above is specific to gpt-oss-safeguard-120b. Do not infer that every model with a similar parameter count has the same requirements, or that smaller models always need a discrete GPU.
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Choose an inference runtime and deployment platform
OpenAI lists vLLM, Ollama, and llama.cpp as compatible inference stacks for gpt-oss and provides setup guidance that also includes Transformers. Treat these as starting points, not a ranking: verify support for your exact model, device, desired interface, and workload in the current runtime documentation.
| Option | What the cited documentation establishes | What to verify for your deployment |
|---|---|---|
| vLLM | Listed by OpenAI as a compatible gpt-oss stack; its server supports OpenAI-compatible HTTP endpoints, including Completions and Chat Completions. | Model and parameter support, hardware configuration, endpoint behavior, security coverage, and the version you intend to operate. |
| Ollama | Listed by OpenAI as a compatible gpt-oss stack. | Current support for your model and device, the interface you need, and operational controls for your environment. |
| llama.cpp | Listed by OpenAI as a compatible gpt-oss stack. | Current support for your model and device, performance under your workload, and integration requirements. |
| Transformers | Included in OpenAI’s setup guidance for gpt-oss. | Current model support, serving approach, hardware fit, and the production operations you will provide around it. |
With vLLM, the documented server starts with vllm serve. Configure the model and options for your environment using the documentation for the version you deploy. An OpenAI-compatible API can reduce client integration work, but compatibility is an interface convenience—not a guarantee that every endpoint, parameter, or model behaves identically to OpenAI’s hosted services. See the vLLM OpenAI-compatible server documentation for supported endpoints and behavior.
When Kubernetes or a packaged platform fits
Kubernetes and packaged inference platforms can standardize how you deploy and operate a service, but their hardware, configuration, and security requirements remain platform-specific. vLLM documents Kubernetes routes for CPU and GPU deployments, including Helm and KServe options. Its guide says CPU use is for demonstration and testing and will not perform on par with GPUs. NVIDIA describes NIM as containerized and deployable on managed Kubernetes services, with reference implementations and Helm charts. Check the target cluster’s GPU scheduling and topology support; NVIDIA notes that tensor-parallel deployments can require peer-to-peer communication support.
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Consult the current vLLM Kubernetes guide and NVIDIA deployment FAQ for the specific platform path and requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Secure the serving service and its supply chain
Do not expose an inference server directly to untrusted networks. Put authentication and authorization at the service boundary, use TLS where required, restrict network reachability, and protect secrets and model artifacts. Review all routes and enabled plugins in the exact runtime version you deploy.
vLLM access controls
vLLM warns that --api-key does not authenticate every route. Its documentation says not to rely on that flag alone and recommends hardening measures such as a reverse proxy. Confirm which routes the key protects in your selected version, then enforce the required authentication, authorization, and network controls in the surrounding environment. Read the vLLM security guidance before exposing the service.
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Distributed serving and NIM
For distributed vLLM serving, inter-node communication is unencrypted by default. If policy requires protected transport, provide the necessary controls externally; network isolation is not the same as encryption. NVIDIA’s deployment FAQ says NIM does not itself support API-key authentication and describes service-mesh controls as the general solution. A container or managed deployment product should not be assumed to supply all of your organization’s authentication, authorization, encryption, or compliance controls.
Include the host, operating system, libraries, container images, network, model files, and secrets in your security review. Establish model provenance, audit logging, access reviews, and a patch process appropriate to your risk requirements.
Test the service, then operate it continuously
Benchmark the exact model, hardware, runtime version, and workload you intend to deploy. The published product and runtime documentation describes features and requirements, not a universal benchmark that predicts performance in your environment.
- Measure time to first token, end-to-end latency, tokens per second, and error rates.
- Check output quality on representative tasks, including the prompts and edge cases that matter to your users.
- Test expected concurrent load and monitor GPU memory and utilization for capacity pressure.
- Patch container images and dependencies; review platform support matrices, security-update policies, and entitlements.
- Back up model artifacts and configuration, define recovery procedures, and keep a rollback path for model or runtime changes.
Repeat these checks when you change the model, runtime, hardware, context limits, or traffic profile. Those changes can affect both performance and compatibility.
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