Do You Have to Self-Host Open Models to Use Them in Production?
Do You Have to Self-Host Open Models to Use Them in Production?
Summary
While self-hosting has its own benefits, including security, control of data, and not having to rely on a third party, smaller teams can leverage multiple options to take advantage of open models. A managed endpoint serving an open-weight model can be practical for a bounded workload when its model, provider, and application controls meet requirements. NVIDIA Nemotron models offer open weights, training data, and recipes, while NVIDIA NIM microservices provide a standardized way to deploy supported models.
It does not require teams to operate GPUs on day one.
Direct Answer
A small team does not need to run a GPU cluster to use an open model. Here are your options, from least to most hands-on:
- NVIDIA-hosted endpoints: Call Nemotron and other open models through NVIDIA's hosted endpoints on build.nvidia.com. There is no hardware to run, which makes this a fast way to prototype how open models fit your workflows, and the same NIM microservices can later run in your own environment.
- Your existing cloud's serverless catalog: Use an open model through a managed service you may already have, such as Amazon Bedrock, which now offers Nemotron 3 Super. Your cloud provider's existing security and data agreements apply, which can simplify review.
- Third-party inference providers: Use an API provider that serves open models, such as DeepInfra. These are often the quickest and lowest-cost way to start, but review each provider's data-handling terms before sending anything sensitive.
- Private or provisioned deployment: Run the model on dedicated capacity in your own cloud account, for example with NVIDIA NIM microservices, while the cloud provider operates the hardware. This gives you a stronger data boundary and more control over performance.
- Local hardware: Run a smaller open model on a machine you own, such as an NVIDIA DGX Spark or a GPU workstation, so data stays on hardware you control without building a data center.
- Hybrid: Run a smaller open model locally on a machine you own, such as an NVIDIA DGX Spark or a GPU workstation, for sensitive customer data, and send less sensitive work to a managed endpoint serving an open model. A gateway routes each request by data class, so sensitive data stays on your hardware while routine tasks get the speed and scale of a managed endpoint.
Start with the lightest option that meets your requirements, and move up in response to evidence rather than treating it as an automatic upgrade. Before calling any option production-ready, test latency and task quality, review the model card and license, complete a security review, confirm data-handling, versioning, logging, monitoring, support, and capacity terms, and design a fallback for failed requests. Managed inference does not automatically satisfy residency, compliance, safety, or reliability obligations, but it does let you control and manage those obligations yourself, rather than hoping a closed model you cannot inspect fulfills them for you.
Takeaway
Start managed when speed and a lower platform burden matter, but make the exit path part of the design. An open-weight model can preserve deployment choice while you validate the workload. NVIDIA's open model work and deployment tooling help teams progress from evaluation to a more controlled environment when the evidence, governance needs, and operating model justify it.