Ollama infrastructure
Best Ollama Hosting: Managed GPU, VPS and Self-Hosted Options
Ollama can be used as a local model runtime and can also access Ollama-hosted cloud models. The right deployment depends on whether you want local model execution, remote GPU infrastructure, a persistent API endpoint, or access to models too large for your own hardware.
Can I run Ollama locally?
Yes. Local execution is Ollama's core use case. The model runs on your own supported machine, which can be useful for privacy, local development, offline workflows, and avoiding per-token API charges.
Can I run an LLM locally using Ollama?
Yes. Ollama provides a local model runtime and model-management workflow for supported language models. Whether a particular model is practical depends on the machine's available memory and compute.
Can I access Ollama from another computer or phone?
A remote client can use an Ollama server when the service is reachable over the network, but remote exposure should be treated as an infrastructure decision. Prefer private networking or a properly authenticated proxy instead of exposing an unrestricted model endpoint directly to the public internet.
Four common Ollama deployment models
| Deployment | Best fit | Tradeoff |
|---|---|---|
| Local Mac or PC | Private local inference and development | Limited by local hardware and uptime |
| CPU VPS | Small models, orchestration, light inference | Large-model inference may be slow |
| GPU cloud | Larger or faster self-managed inference | Higher compute cost and more infrastructure work |
| Ollama cloud models | Models too large for personal hardware | Inference runs on Ollama cloud rather than your own GPU |
Ollama cloud changes the hosting decision
Ollama now supports cloud models through the familiar Ollama CLI and API. That means an application can continue using the Ollama interface while selected models execute on Ollama-hosted infrastructure instead of the local machine.
This can be useful when a model is too large for local hardware, but it is a different architecture from running the model yourself on a VPS or GPU cloud instance.
When a remote Ollama server makes sense
- You want one model server shared by multiple agents or applications.
- The inference hardware should remain online independently of your laptop.
- You want centralized model downloads and runtime configuration.
- Your agent and model server should scale independently.
Do not expose the inference API casually
Treat a remotely reachable model endpoint as infrastructure. Use appropriate network isolation, authentication or proxy controls, firewall rules, monitoring, and encrypted transport rather than exposing an unrestricted service directly to the internet.
Sources
- Ollama cloud models— First-party cloud-model behavior and workflow