How to Run a Local LLM in 2026: A Practical Guide for Businesses

Can a business use an LLM to analyze contracts, source code, or customer requests without sending that data to a third-party AI provider?

Yes – by running the model within its own infrastructure.

That’s why more companies are considering a local LLM as an alternative to cloud-based AI services. This approach keeps sensitive data inside the organization, reduces reliance on external APIs, gives businesses more control over model access, and can make costs easier to manage at scale. And running an LLM locally no longer requires an expensive GPU server – smaller models can run even on standard workstations.

In this guide, we’ll explain how to run a local LLM on Windows, macOS, and Linux, what hardware to choose, and when local deployment makes business sense. We’ve also compared the cost of local LLMs vs. ChatGPT in a separate guide.

Why Businesses are Moving to Local LLMs

The main reason is control over data. When a company relies on an external AI platform, prompts, documents, and internal information may leave the corporate environment. With a local LLM, sensitive data can remain within the organization, which is especially important when working with customer information, source code, financial documents, or internal knowledge bases.

Another advantage is speed and predictability. A local model is not affected by third-party service latency, API rate limits, or sudden pricing changes. For use cases where employees interact with AI frequently – such as customer support, software development, or document processing – this can make day-to-day work faster and more consistent.

Local deployment also gives businesses more freedom to customize models for their needs. Open-source LLMs can be fine-tuned or adapted using company-specific data, terminology, and workflows. This allows organizations to build specialized AI assistants for internal documentation, domain-specific tasks, or coding standards, improving output relevance while keeping control over the AI system.

Finally, running an LLM locally reduces dependence on an external API. The company decides which model to use, when to update it, and how long to keep a particular setup in place. That gives businesses more flexibility and reduces the risk of a critical workflow being disrupted by changes made by an outside provider.

Where Local LLMs Actually Help a Business

A local LLM is valuable not on its own, but when it solves a specific business problem: speeding up employees’ work, helping them find information faster, or enabling AI use while keeping private data inside the customer’s network perimeter. This is especially important for organizations that work with confidential documents, proprietary source code, customer records, financial information, or other sensitive data that should not be transmitted to external AI services.

Customer Support and Chatbots

In customer support, local LLMs can answer common questions, help agents draft responses, and search internal knowledge bases while keeping sensitive data within the company’s infrastructure.

For example, when developing a RAG-powered support chatbot, the SCAND team built a solution that retrieves relevant information from a knowledge base and generates context-aware responses for users. In another project, the AI Knowledge Assistant combined internal document search with a chat interface, enabling the system to handle up to 65% of routine inquiries without specialist involvement.

Developer Productivity

For software development teams, a local LLM can act as an internal AI assistant that helps developers work with code, understand existing logic, and generate technical documentation. One of the key advantages is that source code can remain within the company’s own infrastructure.

For example, SCAND developed a private LLM-based solution for automating source code documentation. This approach is especially useful for large or long-running projects where documentation quickly becomes outdated, and developers spend significant time understanding unfamiliar code.

Logistics and Business Operations

In logistics and operational workflows, LLMs can help employees work with large volumes of data, find relevant information, process requests, analyze documents, and speed up routine decision-making.

For companies dealing with sensitive commercial data, local deployment offers an additional advantage: information about customers, routes, orders, and internal processes remains under the company’s control. One example of practical AI use in this area is SCAND’s AI development solution for logistics.

Documents and Internal Knowledge Bases

Another strong use case is working with corporate documents. Instead of manually searching through dozens of policies, contracts, reports, or internal wikis, employees can ask questions in natural language and receive answers based on company data.

To make this possible, a local LLM is often connected to a corporate knowledge base using RAG. The model does not need to “know everything” in advance: it retrieves relevant documents at the time of the request and generates an answer based on that context. This approach works well for internal search systems, employee AI assistants, and workflows involving confidential company information. Learn more about this approach in SCAND’s RAG development services.

Step-by-step: How to Run a Local LLM

The easiest way to run an LLM locally is to use Ollama. It handles model downloads and execution and works on Windows, macOS, and Linux. Once installed, you can interact with a model directly from the terminal without relying on a separate cloud AI platform.

For the examples below, we’ll use the same model across all three operating systems. Ollama supports many models, so you can later switch to Gemma, Qwen, DeepSeek, Mistral, Kimi, or another option that better fits your use case.

How to Run a Local LLM on Windows

For Windows users, the process is similar to installing any regular application. Ollama runs as a native Windows app, and after installation, the Ollama command becomes available in PowerShell and Command Prompt.

Step 1. Install Ollama

Download OllamaSetup.exe from the official Ollama for Windows page and run the installer.

Step 2. Check that Ollama is installed

Open PowerShell and run:

ollama -v

If the command returns the Ollama version, you’re ready to install and run a model.

Step 3. Run a local model

For example:

ollama run gemma4

The ollama run command starts the selected model. The same approach works with other models available through Ollama.

Once the model starts, you’ll get an interactive chat in the terminal. You can enter a prompt such as:

Summarize the main risks in this contract.

The request is processed by the model running through your local Ollama installation.

Step 4. Check your installed models

To see which models are available locally, run:

ollama ls

To check which models are currently running:

ollama ps

To stop a model:

ollama stop gemma4

For business workstations, it is also worth planning disk space in advance, since model files can take up significantly more storage than Ollama itself.

How to Run a Local LLM on Mac

On macOS, the setup is similarly straightforward, especially on Apple Silicon Macs.

Step 1. Install Ollama

Download ollama.dmg from the official Ollama for macOS page, open the file, and move Ollama to the Applications folder.

Step 2. Verify the installation

Open Terminal and run:

ollama -v

Step 3. Start the model

ollama run gemma4

Once it starts, you can immediately interact with the model from the terminal.

For example:

Create a short summary of this customer request and list the required actions.

To view installed models, use:

ollama ls

To check which models are currently running:

ollama ps

These commands work the same way on macOS, Windows, and Linux.

How to Run a Local LLM on Linux

On Linux, installation is slightly more technical, but the basic setup still takes only a few steps.

Step 1. Install Ollama

Open the terminal and run the official installation command:

curl -fsSL https://ollama.com/install.sh | sh

Step 2. Check the installation

ollama -v

Step 3. Make sure Ollama is running

On systems that use systemd, you can start and check the service with:

sudo systemctl start ollama

sudo systemctl status ollama

Step 4. Run the LLM

The command is the same as on Windows and macOS:

ollama run gemma4

You can now send prompts to the model directly from the terminal or using inbuilt Ollama’s UI.

If you later want to use the local LLM inside your own application rather than as a terminal chatbot, Ollama also provides a local API. By default, it is available at http://localhost:11434/api, which means the same local model runtime can be connected to a corporate chatbot, internal search system, or another business application.

The basic workflow is therefore almost identical across all three platforms: install Ollama → choose a model → run ollama run → start working with the LLM locally. The main differences between Windows, macOS, and Linux come down to installation and how Ollama is managed as an application or system service.

Other Tools Worth Knowing About

Ollama is one of the easiest ways to get started, but it is not the only option. If you prefer a graphical interface, need more control over how a model runs, or want to work with local documents without writing code, several other tools are worth considering.

Other Tools to Consider

There is no single “best” local LLM tool. LM Studio is convenient for visual experimentation, llama.cpp gives developers more control, while GPT4All is a straightforward option for local chat and document-based use cases. For the step-by-step examples in this guide, however, we use Ollama because it provides a similar workflow across Windows, macOS, and Linux.

What Hardware Do You Need?

The good news is that you do not need an expensive GPU server to run a local LLM. Smaller models can run on a modern laptop, and available memory is often the main limitation. As a practical rule of thumb, 8 GB of RAM is enough for experimenting with small models, 16 GB gives you considerably more flexibility, while medium-sized models are better suited to systems with 24 – 32 GB or more. Exact requirements depend on the model and context length: the larger the model and the more text it needs to process at once, the more memory it will require.

A powerful GPU is not mandatory. LLMs can run on a CPU, although responses will usually be generated more slowly. A GPU becomes more important when you need faster inference, want to run larger models, or expect several users to access the model at the same time. Ollama, for example, can use supported NVIDIA, AMD GPUs, and Apple’s M hybrid chips to accelerate local inference.

You can also reduce hardware requirements by using quantized models – compressed versions that use lower-precision weights. 4-bit and 8-bit quantization can significantly reduce memory usage, making it possible to run larger LLMs on more affordable hardware, sometimes with a small trade-off in quality or performance.

Which Open-source Model Should You Run?

There is no single “best” local LLM. A model that works well for coding may be unnecessary for a simple internal chatbot, while a powerful reasoning model may require far more hardware than a small business actually needs. The practical approach is to choose the smallest model that performs your task well enough. For more background on how these models work, see our guide to building and training large language models.

Llama. You will still see Llama 3 and especially the compact Llama 3.2 models in many local setups. They remain useful when you need a relatively lightweight model for text generation, summarization, or an internal assistant. However, Meta has since introduced Llama 4 Scout and Maverick, which add native multimodal capabilities but also target considerably more powerful hardware. For a laptop or workstation, a smaller Llama 3.x model can therefore still be the more practical choice.

Mistral. A good option when you want a versatile model for everyday business tasks. The current Mistral Small 4 combines general chat, coding, reasoning, and image understanding in one model, making it suitable for assistants, document analysis, and developer workflows.

DeepSeek. Best known for strong reasoning and coding capabilities. In 2026, the family has already moved to DeepSeek V4, while smaller distilled DeepSeek-R1 variants remain much easier to experiment with locally. Those distilled models range from 1.5B to 70B parameters, so businesses can choose a version that better matches their available hardware.

Qwen. A particularly flexible choice if you need different model sizes or work in multilingual environments. Qwen3 is available in variants ranging from very small 0.6B and 1.7B models to much larger models, with capabilities covering general tasks, coding, math, and reasoning. That makes it easier to match the model to the hardware instead of upgrading the hardware to fit the model. Models with “coder” suffix are specifically trained for coding sessions and are much capable in this.

Gemma. Developed by Google, Gemma is a family of relatively lightweight open models designed to run efficiently on local hardware. Gemma 3 is particularly practical for laptops and workstations, offering text generation, reasoning, coding, and native multimodal capabilities in a compact model family. Its smaller variants, including 1B and 4B models, are well suited to local assistants, summarization, and experimentation, while larger versions provide stronger reasoning and coding performance when more GPU memory is available.

Phi. Microsoft’s Phi family is designed around smaller, more efficient models, which makes it interesting for local, edge, and resource-constrained deployments. Phi-4-mini focuses on text and reasoning, while Phi-4-multimodal can work with text, images, and audio. For companies that value a smaller footprint over maximum model size, Phi is a strong place to start.

For a first local experiment, start small. A compact Llama, Gemma, Qwen, DeepSeek-distilled, or Phi model is usually enough to test whether the use case works. Move to a larger model only when the improvement in answer quality justifies the additional memory, infrastructure, and operating cost.

Private & Self-hosted LLMs for Enterprises

For a small team, a local LLM may simply be a model running on a single computer. For a large company, the challenge is different: the model needs to work securely with corporate data, serve hundreds of employees, and still remain under the organization’s control.

The main difference between a public LLM and a private LLM is who controls the infrastructure and the data. With a public AI service, a company sends requests to an external provider and operates under that provider’s rules. A private or self-hosted LLM runs in infrastructure controlled by the organization itself – on-premises, in a private cloud, or in an isolated environment. In some cases, the model can operate without access to external services at all, which is why an offline LLM enterprise approach is attractive to companies with strict confidentiality requirements.

Starter hardware needed for inferencing local models:

• Apple devices with M-series chips, for example: Mac Studio or even Mac Mini with at least 32GB RAM;
• PC with starter Nvidia cards: RTX-4090, RTX-5090, RTX 6000. Or 2 or more cards of the same type in SLI mode.

More serious ones like A100/H100/H200 are needed for advanced usage of LLM for bigger models and multiple users.

However, self-hosting is not just about installing a model on a corporate server. For an enterprise deployment, it is important to define:

• who can access which models and data;
• how prompts, documents, and interaction history are protected;
• which user actions need to be logged and monitored;
• how models can be updated without disrupting business workflows;
• how the system will scale as the number of users and requests grows.

That is why a private LLM usually becomes part of a broader enterprise AI infrastructure rather than a standalone application. If you want to explore the process in more detail, see our step-by-step guide to building a private LLM.

And if your project has already moved beyond experimentation and you need to design, integrate, and deploy a private AI system around your company’s requirements, SCAND provides private LLM development services – from model and architecture selection to integration with internal data and existing business systems.

Keeping Your Local LLM Running Smoothly

Running a local LLM is only half the job. Over time, you need to update models, monitor response speed, memory usage, and output quality, and check whether newer versions are better suited to your use case. For a small setup, this can be handled manually, but at the enterprise level, these processes are better automated and managed systematically. If you need that kind of infrastructure support, learn more about our MLOps consulting and development services.

Reducing Local LLM Costs

A local LLM is not always cheaper than a cloud API, especially if it requires dedicated GPU infrastructure. That is why it makes sense to start with the smallest configuration that can reliably handle the task: choose a model that fits the use case, use quantized versions, avoid keeping large models running when they are not needed, leverage from capabilities to host several users in parallel, and monitor actual hardware utilization.

For small teams, a single workstation may be enough, while enterprise systems can benefit from shared infrastructure used by multiple applications and teams. The key principle is simple: do not run a larger model than the business task actually requires. This helps reduce memory requirements, power consumption, and operating costs.

Mobile & Edge: Running LLMs Beyond the Server

Local LLMs can run not only on workstations and enterprise servers. Compact models are increasingly being deployed directly on smartphones, tablets, and edge devices, where they can process data without constantly sending requests to the cloud.

This approach is especially useful for applications that require stronger privacy, fast response times, or reliable operation with limited connectivity. Learn more in our guide to on-device LLMs and our practical article on building a local LLM mobile app.

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