Why Your SME Should Run AI In-House

Message from William Nicholls, CEO of Maltix

You've probably heard about Large Language Models (LLMs)—the technology that powers tools like ChatGPT—but you might think they're only for massive corporations. 

Not true! 

By running these models on your own servers or even powerful desktop machines, your business can gain a significant competitive edge.

The main reasons for considering this are simple: privacy, control, and money.

Absolute Data Privacy & Security:-

When you use a big cloud provider's AI, your sensitive company and customer data has to leave your premises. 

With a localised LLM, your information never leaves your own secure network. This is crucial for meeting strict UK and global data protection rules (like GDPR) and keeping client information confidential.

Cost Efficiency:- 

While there's an initial cost for hardware, the long-term running costs are significantly lower than paying recurring, monthly cloud usage fees. For continuous or heavy AI use, this saves a substantial amount of

Capital.Operational Independence:-

Say goodbye to worrying about a third-party service outage, sudden price hikes, or unexpected changes to their rules. Running it yourself means your AI services are always on and under your control.

Performance & Latency:-

 All the computation happens right where you are. This eliminates network delay and makes your AI applications real-time, which is essential for fast customer service or immediate process automation.

Customisation & Integration:-

Running LLMs locally makes it easier to fine-tune models on your proprietary data and integrate them deeply with existing workflows. This aligns the AI precisely with your company's specific terminology.

This isn't just about saving money; it's about making your business smarter, faster, and more unique.

• Tailored AI for Your Business: Running your LLM locally allows for deep customisation and fine-tuning. You can train the AI specifically on your company's documents, processes, and even internal jargon.

• Automate Internal Knowledge: Integrate your internal documents, guides, and proprietary resources. Your local LLM instantly becomes the ultimate, organisation-specific knowledge assistant for your staff (through methods like Retrieval Augmented Generation).

• Enhanced Compliance: For businesses in finance, healthcare, or other regulated markets, keeping the data within the regulatory boundary of your own country and premises is a massive selling point and a legal necessity (data sovereignty).

• Rapid Development & Experimentation: Your developers can test, innovate, and deploy new, customised AI applications faster without needing permission or paying extra fees to a third party.

• Secure Customer Service Automation in regulated industries.

• Domain-Specific Knowledge Retrieval for legal, medical, or technical staff, based only on in-house data.

• Cost-Effective Automation across various departments with moderate compute resources.

• Personalised Staff Training and Onboarding using your company's proprietary data.

By running your LLMs in-house, you gain greater flexibility, privacy, and long-term cost control. 

You are positioning your SME to use AI in a way that is perfectly aligned with your unique business needs and values.

Don't just use the AI giants' models; build your own intelligence.

An explanation of the tech jargon - 

Go talk to a man who can

Cost of investment ? £1500 to £2000 ish

VRAM (Video RAM):-

The memory on the GPU. This is the primary bottleneck. If the model doesn't fit here, it runs very slowly. 

Rule of thumb:-

You need roughly 4GB of VRAM per 7 Billion parameters when the model is optimised (quantised).

Quantisation:-

A technique to compress the LLM's file size (e.g., from 16-bit to 4-bit) so it fits onto smaller VRAM, with only a minimal loss in quality. This is what makes local LLMs viable for SMEs.

Inference Speed:-

Measured in tokens/second. This is how fast the model generates its response. A speed of 20+ tokens/second is considered excellent for real-time, interactive use.

System RAM (DDR5):-

The main computer memory. While less critical than VRAM, you need enough to load the operating system and manage all the data. 

32GB is a solid starting point.Storage (NVMe SSD)

The drive that stores your models and data. NVMe SSDs are essential for fast loading times.

Models like Llama 3 can take up 10GB to 140GB of disk space, so plan for at least a 1TB drive.