How do I run a private LLM on Runpod with vLLM or Ollama?

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A private LLM, in this context, means running an open-source language model yourself, on hardware you rent and control, rather than sending your data to someone else's AI product through an API. Runpod is a GPU cloud rental service that makes the hardware side of this affordable without buying a machine outright. vLLM and Ollama are the two most common tools for actually serving the model once you have a GPU: vLLM is an open-source inference engine built for speed and handling many requests at once, aimed at production use, while Ollama is an open-source tool built for simplicity, downloading and running a model with one short command, aimed at quick setup and casual or lower-volume use. This guide covers both paths.

Before you start: choosing a model and GPU

Model size drives your GPU choice more than anything else. A rough rule of thumb: a model needs about 2 gigabytes of GPU memory per billion parameters when running at standard precision, less if it's been quantized (compressed to run in less memory, usually with a small quality trade-off). A 7 to 8 billion parameter model (Llama 3 8B, Mistral 7B and similar) fits comfortably on a 24 gigabyte card. A 13 billion parameter model needs more headroom, and anything in the 70 billion range needs a much larger card or multiple GPUs working together.

For most small business use cases, test, a document summarizer, an internal chatbot, a classification task, a 7 to 13 billion parameter model on a single mid-range GPU covers it without needing enterprise-scale hardware. For a document use case, see using AI on company documents without sending them out.

Step by step: running a model with Ollama

1. Rent a Pod on Runpod with a GPU sized to your model, an RTX A5000 or RTX 4090-class card for a 7 to 8 billion parameter model, choosing a template with Ollama pre-installed if one's available, or a standard Linux template otherwise.
2. Install Ollama if it's not already on the template, which is a single command on Linux, then start the Ollama service.
3. Pull the model you want to run with a single command referencing the model's name, which downloads it from Ollama's model library.
4. Run the model and test it directly from the terminal to confirm it's responding before connecting anything else to it.
5. Expose it as an API if you need another application to call it, which Ollama supports natively over a local port, then make sure that port isn't left open to the public internet without authentication in front of it.
6. Stop the Pod the moment you're done testing or the workload finishes, since it bills by the second until stopped.

Step by step: running a model with vLLM

1. Rent a Pod on Runpod sized to your model, using a template with the right CUDA and Python environment already set up, which saves a meaningful amount of setup time over a bare machine.
2. Install vLLM through its standard Python package installer if it's not already included in your template.
3. Launch vLLM's OpenAI-compatible server pointing at your chosen model, which downloads it automatically on first run and starts serving it over an API that mimics a familiar request format, making it easy to swap into code that already expects that structure.
4. Test it with a real request to confirm it's returning sensible output and running at an acceptable speed for your use case.
5. Put it behind proper access controls before connecting it to anything production-facing, an API key check at minimum, since vLLM's server doesn't assume you want it open by default.
6. Stop the Pod when the workload is done, or move to Runpod Serverless if traffic is irregular enough that a continuously running Pod would waste money sitting idle. My Runpod Serverless guide covers that setup.

vLLM or Ollama: which one

Ollama wins on setup speed and simplicity, genuinely a few minutes from a rented machine to a running model, which makes it the better starting point for testing an idea or running something for a single user or a small internal team. vLLM wins on throughput once you have real, concurrent traffic, multiple users hitting the model at once, since it's specifically built to batch and serve requests efficiently at that scale. A reasonable path: start with Ollama to validate the idea cheaply, then move to vLLM if usage grows into something that needs to serve several people reliably at once.

Cost estimate

As of October 2026, an RTX A5000 on Runpod's Community Cloud runs $0.16 an hour, and an RTX 4090 runs $0.34 an hour, either of which comfortably handles a 7 to 8 billion parameter model. A 13 billion parameter model fits better on something like an A40 or L40 at $0.35 to $0.69 an hour. Testing and light, occasional use at a few hours a week costs a few dollars a month. A model running close to continuously for real production traffic on a mid-range card can land anywhere from roughly $100 to $500 a month depending on the card and hours, which is where comparing a Pod running continuously against Runpod Serverless's scale-to-zero model becomes worth doing. Full rates are in Runpod pricing for GPU costs, serverless and storage.

Common setup problems

• The model won't fit in GPU memory. This is usually a sign the chosen GPU's VRAM is too small for the model at the precision you're running it at. Either move to a card with more VRAM or use a quantized (compressed) version of the model, which trades a small amount of quality for a much smaller memory footprint.
• Responses are slower than expected. Check whether the model is actually running on the GPU rather than falling back to the CPU, a common misconfiguration that dramatically slows every response without necessarily throwing an obvious error.
• The API is reachable from outside your network when it shouldn't be. Both Ollama and vLLM's servers are straightforward to expose over a port, which means it's just as straightforward to accidentally leave that port open to the public internet. Confirm your access controls before connecting anything beyond your own testing.

Privacy limits: the honest part

Running your own model this way keeps your data out of a third party's AI product, meaning it's not training anyone else's model and isn't passing through a company whose primary business is AI. That's the real privacy win, and it's genuine. What it doesn't do is make this fully private in the sense of "data never leaves a machine I physically control." Your data and the model both run on hardware rented from Runpod, which means Runpod's infrastructure, not your own office server, is handling it. For most small businesses, this is a perfectly reasonable level of privacy. For anyone with strict regulatory requirements (certain health or financial data, for example), confirm Runpod's own data handling terms directly and consider whether an on-premises setup, a physical server you own and control outright, is a requirement rather than a preference before committing sensitive data to any rented cloud GPU. For the wider picture, see self-hosted AI for small business.

How I'd approach this

When a client wants a private AI feature, their own documents searchable by an internal assistant, a customer-facing chatbot that doesn't send conversations to a third party's AI platform, I set up the actual model, the GPU sizing, and the access controls properly, and I'll tell you honestly where the line between "private from other AI companies" and "fully on-premises" actually sits for your specific data. Get in touch if you want this built and secured properly rather than assembled from tutorials.