The benefits of using local language models over AI giants
Most businesses are now utilising some form of AI. Whether it’s helping to write up meeting notes, summarise reports or support with the more complex aspects of a business, it’s becoming ingrained in the day-to-day.
And, often, the AI giants (think OpenAI’s ChatGPT and Anthropic’s Claude) are the natural go-to. But, while both are very powerful tools, these large language models aren’t the only option.
If you’ve not come across them before, local language models are a smaller, less resource-intensive alternative. They’re models that run on your own machine instead of a distant data centre. Here, we’ve summarised how they might be more practical than you think.
WHAT THEY ARE
A local or small model (SLM) is a scaled-down version of the large language models most people know. They’re typically created from large language models using techniques like knowledge distillation, pruning, and quantisation, which reduce a model's size and computational demands while preserving as much of its performance as possible (plus, it makes them light enough to run on a standard laptop).
And one of the key differences is ownership because, once a small model is downloaded, it's yours. No data leaves your network. No provider can change its terms, raise its prices, or restrict access overnight. Many are open source, maintained by an active developer community and built for specific jobs rather than general use.
A good example is Whisper Small, a tiny speech-to-text model used for incredibly accurate transcription. Essentially, it’s a focused tool built for one job, rather than a general-purpose system that has been repurposed for it.
SPECIALISTS, NOT GENERALISTS
Which leads us nicely into the main difference between cloud-hosted giant models and smaller, local models. Large models like Claude and ChatGPT are massive generalists, built to reason across almost any topic. Small local models, on the other hand, are often fine-tuned into specialists: stripped of extraneous data so they can excel at one specific job while staying fast and lightweight.
A model doesn't need access to the whole of human knowledge to check a piece of code for faults or answer a question about a client record. A well-chosen, small model, running locally, does that job well without the overhead of a much larger system (if it’s been trained right!).
SECURITY AND COSTS
One of the biggest benefits of these local models is the security element. For regulated or client-sensitive work, a local model might be preferable because it never sends information over a network. It stays inside a closed loop on your own system and no data leaves the building.
Meanwhile, a cloud provider's certifications don't change the fact that the service is hosted under another country's laws, exposed to regulatory decisions outside.
And there’s a financial argument, too. Many subscription AI services currently run at a loss, propped up by investment while the market works out their long-term value. As costs rise, that's likely to affect pricing and terms for the businesses that depend on them. A model running locally costs nothing beyond hardware you already own.
ENVIRONMENTAL IMPACT
There’s a lot of concern over the environmental impact of using large language models, too. Right now, the consumption of electricity and water, used to cool large data centres, is a cause for major concern.
That’s why using a massive cloud model for a simple task is highly inefficient, particularly when the same job can be done well enough - if not better - by a highly-optimised, local model. They only consume energy proportional to the specific, smaller task they are performing, usually run straight from your own system. Nowhere near the same impact seen at scale for bigger models.
WHERE THEY FALL SHORT
We know small models can’t be used for absolutely every job, they’re just not built that way. While large models come with supporting infrastructure (such as memory, automatic correction of a messy prompt, extended inference time), raw local models are less forgiving.
For instance, a prompt in a small model might need to be very precise to get the answers you need. Even then, while results are often good enough for the job you need, they’re rarely as deep. The right approach is using each tool where it earns its place, not treating either as a universal replacement: local models for fast, private, specialised tasks, and large cloud models for complex reasoning.
WHERE THEY REALLY SHINE
The most useful application is embedding a small model inside custom software, paired with a system that indexes an organisation's own data (retrieval-augmented generation, or RAG).
A great example of this is when you want to understand data you’ve already collected yourself, without outside interference. A bit like Google’s Gemini Notebook (previously NotebookLM), which is trained to just look at files you’ve specifically uploaded, it can reduce the chance of hallucinations or the addition of external information.
Something like this can help you answer questions to find the information that’s already there, without hours spent digging for it, and create insights to help you move forward. There’s no fabricating an answer it doesn't have and, if the information isn't there, it says so.
Another way they really shine is with Fine Tuning. SLMs can be easily adapted to excel at niche tasks by training them on specialised, domain-specific data. For instance, fine-tuning an SLM on medical records can create a clinical note assistant, while training one on customer support logs can build a tailored automated agent.
While fine-tuning shapes the model’s tone, style, and domain mastery, RAG anchors it to real-time, verified external data. And when building intelligent systems? You can mix and match these techniques to great effect.
USING LOCAL MODELS AT BUZZ
We use AI where it adds genuine value, and that’s how we’ve approached every significant technology shift over the past two decades.
For us, our internal tools are utilising these local models already, meaning less dependency on third-party providers, client data kept inside a closed system, and less exposure to the cost and risk of cloud-hosted alternatives. For specific jobs, they’re the natural go-to, and are proving invaluable already. We’re excited to see how we can make the most of them as technology continues to develop.
That doesn’t mean we’re not undertaking more complex work with larger models, but we’re using them where it makes the most sense, and gives the most value to our team.
If you’re wondering where to start, we can help you build and integrate similar small and local language models into your own workflow, get in touch.
Our bespoke software development is all about helping you utilise tools in ways that deliver real change to your business, from saving time to improving the bottom line.
