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Ollama: Simplifying Local LLM Deployment

IBM Technology
IBM Technology
29 Jun 2025
AI-Generated Summary
Artificial Intelligence
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Reading time: 5 minutes

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By now, you’ve probably tried out some really helpful AI models.0:00
But at the end of the day, I was using someone else's cloud computing resources.0:11
Now, what if I told you, though, that there's an open source way to run AI models and LLMs locally?0:23
Today we're gonna be taking a look at Ollama to run large language models or LLMs from your old machine.0:44
This is all simplified with Ollama through a single command, Ollama run.2:10
Ollama has a catalog of standardized and customizable language, multi-model embedding and tool calling models.2:53
Now, beyond just using the Model Catalog, you can actually take advantage of what's known as the Ollama model file.4:34
You can use Ollama and the power of open source AI to use and manage LLMs from your local machine.6:31

Ollama: Simplifying Local LLM Deployment

Imagine running powerful AI models right from your own machine—no cloud services required. With Ollama, you can harness the capabilities of large language models (LLMs) locally, saving costs and enhancing data privacy.

Easing into Local AI

By now, you’ve probably experimented with some remarkable AI models, whether for data summarization, pair programming, or more. Traditionally, this meant relying on cloud services and external APIs. But imagine having full control over your compute resources and sensitive information.

"What if I told you there’s an open-source way to run AI models and LLMs locally from your own machine?"

Instead of sending your data off to remote servers, you keep everything on-premises. That means faster iterations, predictable costs, and heightened privacy for your most critical data assets.

Getting Started with Ollama

So, how does it work? First, download the Ollama Command Line Interface (CLI). Whether you’re on Windows, macOS, or Linux, visit ollama.com and grab the installer tailored for your system. The CLI becomes your gateway to running, downloading, and interacting with models—all from a familiar terminal environment.

In previous setups, you might have navigated repositories like Hugging Face, manually fetching model weights and wrestling with configurations. Ollama streamlines this: you type ollama run <model-name>, replacing <model-name> with choices like granite, llama, or deep-seek. This single command not only downloads a compressed, optimized model but also spins up an inference server locally. Almost instantly, you drop into a GPT-style chat interface, ready to experiment.

Think of ollama run as the AI equivalent of a package manager—install, upgrade, and run models with one line.

Exploring Ollama’s Model Catalog

What makes the Ollama catalog stand out is its range of standardized and fine-tunable models designed for distinct workflows:

  • Language Models: Ideal for conversational agents, text generation, and instructional Q&A.
  • Multi-Modal Models: Process images alongside text to unlock tasks like visual analysis.
  • Embedding Models: Convert documents (like PDFs) into vectors for database indexing and semantic search.
  • Tool Calling Models: Fine-tuned for interacting with APIs and external services in an agentic way.

Each category addresses a unique set of requirements. This rich selection of models means you can prototype quickly and iterate on which architecture best suits your data patterns without leaving your development environment. Popular choices include the versatile Llama series and IBM’s enterprise-ready Granite model.

Leveraging the Ollama Model File

Beyond the catalog, Ollama uses a model file concept similar to Docker images. A model file encapsulates all the weights and dependencies needed for inference. You can import community models from Hugging Face, or start with one you’ve customized using system prompts and hyperparameters. No more manual weight conversions or dependency conflicts—just point your model file and go.

Under the hood, every request targets the Ollama server running on localhost at port 11434. Whether you call it from the CLI or via HTTP, your prompt hits a REST endpoint, and the server handles all the heavy lifting.

Simplifying Development Tasks

The genius of Ollama is how it abstracts LLMs as local APIs. For instance, if you’re building an application with frameworks like LangChain, you simply direct your POST requests to http://localhost:11434. The interaction feels no different than calling an external AI service, yet everything stays within your network perimeter.

Need to run Ollama on a remote box? SSH in, forward the port, and you can make requests from anywhere. Want to combine Ollama with Open Web UI? Feed in PDFs or other documents to build a RAG (Retrieval-Augmented Generation) pipeline. The flexibility lets you integrate local AI seamlessly into your existing toolchain.

Why Choose Ollama?

Whether you’re looking to reduce cloud expenses, protect private data on-prem, or deploy LLMs on edge devices with intermittent connectivity, Ollama bridges the gap between open-source innovation and developer-friendly tooling. While there are other options for local AI, Ollama’s simplicity and extensible catalog make it a standout for many projects.

Conclusion

Use Ollama to take full control of your local AI deployment:

  • Bold Actionable Takeaway: Run ollama run llama today to experience how fast and secure local LLMs can be.

So, how do you envision using local AI in your next project? Have you given Ollama a spin yet?

Topics:

ollamalocal-llmsopensource-ai