Using Local LLMs & Open-Source Models

Local LLMs in one sentence

A local LLM is a large language model whose weights you download and run on your own hardware – on a laptop, a workstation, or an in-house server – so that no request and no document ever leaves your machine or your own network.

The difference from a cloud assistant is fundamental: you're not renting a service, you're operating software. That shifts everything – cost from an ongoing fee to hardware, responsibility from the provider to you, data protection from a contract question to a technical fact.

Why go local at all?

Four reasons make the effort worthwhile:

• Data protection as architecture. Whatever never leaves the machine can't leak, can't be read by anyone else, and can't be used for training. For client data, patient records, contracts, or unreleased product data, that's a structural advantage, not a contractual promise.
• Predictable costs. After the initial purchase, each individual request costs nothing. At high volumes – bulk classification, text processing across tens of thousands of records – the math quickly tips in favor of self-hosting.
• Independence. No provider can shut down your model, raise its price, or change its behavior on you. What works today works identically in a year.
• Offline capability. No network, no problem. Relevant for field deployments, air-gapped networks, and production environments.

How a local model works technically

A language model consists of weights – billions of numerical values produced during training. To generate text, those weights need to be loaded into memory, ideally into graphics memory. That's exactly what determines what runs on your hardware.

There are two figures you need to know. The parameter count – given in billions, such as 8B or 70B – describes the size of the model and, roughly, its capability. The quantization describes how much precision each individual weight value is stored with. At full precision, a model needs roughly two gigabytes of memory per billion parameters; reduced to four bits, it's roughly 0.5 to 0.7 gigabytes. The quality loss at moderate quantization is surprisingly small, while the memory savings are dramatic.

Practical rule of thumb: A stronger model at heavy quantization almost always beats a weaker model at full precision. Given the choice, go for the bigger model with fewer bits. How language models fundamentally work is explained in the article How Does AI Work?.

The tools for getting started

Ollama is the fastest way into operation. One install package, one command in the terminal, and the model is running – including automatic download, memory management, and a local interface that your own applications can use to talk to the assistant. That interface is the real reason Ollama has become standard in development teams: existing software written for cloud models can often be pointed at the local model just by changing an address.

LM Studio pursues the same goal with a graphical interface. Models are browsed in a catalog, loaded with a click, and used in a chat window; parameters like context length and output length are accessible via sliders. For anyone who doesn't want to open a terminal, that's the more comfortable way in – and the built-in compatibility indicator stops you from loading a model your machine simply can't handle.

Beyond that, it's worth looking at lightweight chat interfaces that sit in front of Ollama and offer ChatGPT-like operation in the browser, as well as server-side runtimes for multi-user operation within a company. It makes sense to start on a single machine, though.

Which model fits which hardware?

The decisive figure is the available graphics memory – on Apple computers, the shared system memory. This rule of thumb has proven itself in practice:

Two additions: pure CPU execution without a graphics card works, but is so slow that it's only good for background batch processing. And Apple computers with shared memory are unusually economical for this purpose, because the model gets access to the entire system memory.

Among the model families themselves, there's a lively selection of freely available weights – from European providers to large US corporations to Asian labs, plus specialized variants for code, multilingual work, or especially compact sizes. How these families stack up overall is covered in the article AI Models Compared.

Four steps to a running model

1. Check your memory. Determine your graphics memory or shared system memory and choose the model size from the table above.
2. Install a tool. Ollama for background operation and for your own applications, LM Studio for the graphical interface.
3. Load and test a model. Start with a mid-size quantized variant and test it with three real everyday tasks – not with gimmicks.
4. Set context and output. Context length determines how much text the model sees at once, and it costs memory. This is the most common reason for unexpectedly slow or cut-off answers.

Where local models pay off in business

Preprocessing sensitive documents. Summarize, anonymize, and structure contracts, applications, or reports locally – and only pass the cleaned-up result to a stronger cloud model if needed. This two-stage approach combines data protection with quality.

Bulk processing. Categorizing product data, pre-sorting support tickets, evaluating free-text fields. Where hundreds of thousands of records pile up, self-hosting clearly beats the usage fee.

Internal knowledge search. A local model combined with a search layer over your own documents – the answer is generated from your content without it ever leaving the building.

Development and testing. Build applications against a local model without racking up fees on every test run.

Limits compared to cloud models

Quality gap on complex tasks. On multi-step reasoning, very long contexts, and demanding code, the strongest cloud models still lead. The gap has narrowed over the years, but it's real.

Speed. On a laptop, a large model responds noticeably slower than a cloud service. That's disruptive for conversational work, barely noticeable for batch processing.

Missing extra features. Web search, file processing, image generation, and agent capabilities you have to build yourself. A cloud assistant like ChatGPT ships with all of that built in.

Operational overhead. Updating models, managing memory, planning capacity for multiple users – that's a job someone has to own. Anyone looking for a comparison to hosted options will find it in the article ChatGPT Alternatives.

What "local" really means for data protection

A locally run model processes data without transmitting it to any third party – that's the strongest technical protection conceivable, and it sidesteps the whole discussion about third-country transfers. Three points nonetheless remain your responsibility.

First: the interface you use also has to work locally. Some chat applications send telemetry or reach out to online services for extra features. Second: the machine itself needs access protection and encryption, otherwise you're just relocating the risk. Third: even a local model produces output that can be wrong – the data protection question is solved, the quality question isn't.

For documentation purposes: self-hosting substantially simplifies your record of processing activities, but it doesn't replace it.

Conclusion

Local language models aren't a replacement for cloud assistants, but their complement exactly where data can't leave the building or where volume blows past the fees. Getting started with Ollama or LM Studio is a matter of minutes; the real work lies in honestly sorting out which task gets handled well enough locally and which needs a strong cloud model. Anyone who draws that line cleanly gets both: control over sensitive data and top-tier quality where it counts.