Interest in how to run a local AI model in your organisation has grown sharply over the last eighteen months. The reasons are not hard to find. Data leakage concerns, supplier lock-in, and the creeping cost of API-based AI services have pushed many organisations to ask a simple question: can we just run this ourselves? The answer is yes, with some important caveats. This article walks through what that actually involves, written for people who need to make the decision, not build the system.
What “Local AI” Actually Means
Running a local AI model means the model runs on hardware you control, whether that is your own servers, a private cloud, or a dedicated on-premise machine. Prompts and responses never leave your environment. No third-party API receives your data. The model is, in effect, a piece of software sitting inside your own infrastructure rather than a service you call out to.
This is meaningfully different from using ChatGPT, Claude, or Microsoft Copilot, where your inputs travel to an external provider. If you want to understand that distinction in more depth, Public AI vs private AI: what organisations get wrong covers the practical differences and where organisations typically misread the risk.
The Hardware Reality
Local AI is not free. Running a capable language model requires meaningful compute. Smaller models, in the 7 billion to 13 billion parameter range, can run on a modern workstation with a good consumer GPU. Larger models, capable of genuinely useful business tasks, typically need 24GB of GPU memory or more, sometimes across multiple graphics cards.
For most organisations without existing GPU infrastructure, the realistic starting point is either a single high-spec server or a small cluster. Cloud providers offer GPU instances, but at that point you are back to a hosted environment, which changes the data sovereignty picture. If true on-premise is the goal, budget for the hardware accordingly. A capable single-node inference server costs somewhere between £3,000 and £20,000 depending on the model size you want to support.
The Model Landscape
The open-weight model ecosystem has matured considerably. Models from Meta (Llama), Mistral, and others are freely available and genuinely capable for tasks like document summarisation, internal Q&A, drafting, and classification. Tools like Ollama make it straightforward to download and run these models locally without deep technical expertise.
The gap between open-weight models and frontier commercial models like GPT-4o or Claude is real but narrowing. For tightly defined internal tasks, many organisations find the smaller local models more than adequate. The trade-off is that you bear responsibility for model selection, updates, and performance, rather than outsourcing that to a provider.
What Running a Local Model Solves
The primary benefit is data control. Sensitive documents, client information, and commercially confidential material stay within your perimeter. That matters significantly for regulated sectors where data residency and processing location affect compliance obligations. It also matters for any organisation that has experienced, or is concerned about, the most common AI data leakage scenarios that arise when staff use public AI tools with sensitive inputs.
Beyond data, local models offer cost predictability. There are no per-token charges. Once the infrastructure is in place, the marginal cost of additional usage is essentially electricity and maintenance. For high-volume use cases, this arithmetic shifts quickly in favour of local deployment.
What Running a Local Model Does Not Solve
This is the part that sometimes surprises organisations. Local deployment does not automatically mean safe or well-governed AI. You still need to define what the model is permitted to do, who can use it, and what data it should and should not process. You still need to think about how outputs are reviewed and what happens when the model produces something wrong or harmful.
You also take on responsibilities that cloud providers currently handle for you: keeping the model updated, monitoring for misuse, and managing access controls. The governance burden does not disappear. In some respects it increases, because there is no vendor to point to if something goes wrong.
How Mikka Shows This in Practice
Black Chili built Mikka, our own internal AI assistant, on exactly this basis. Mikka runs on local infrastructure, uses open-weight models, and operates within defined guardrails that control what it can access and how it responds. The point was not just cost or convenience. It was to understand, from the inside, what it actually takes to deploy AI responsibly and maintain that responsibly over time.
The lessons from that process inform how we advise clients. You can read more about the thinking and build decisions in Mikka: building our own AI responsibly. The short version is that the technical deployment is often the easier part. Governance, access policy, and ongoing oversight are where most organisations underinvest.
The Governance Implications
Choosing to run a local AI model is a meaningful governance decision, not just a technical one. It shifts accountability inward. Your organisation controls the model, which means your organisation is responsible for its behaviour. That requires a clear acceptable use policy, defined access controls, a process for reviewing AI outputs in sensitive contexts, and a plan for what happens if the model causes harm or produces a significant error.
None of that is insurmountable. But it should be part of the decision from the outset, not bolted on after deployment when something has already gone wrong.
If your organisation is considering local AI deployment and wants to understand where your current governance stands before you build, the AI Governance Gap Assessment identifies exactly where those gaps sit.
