The Complete Guide to Running AI on Your Own Hardware

If you've read our post on why we run AI locally instead of using the cloud, you already know the benefits: total privacy, zero ongoing costs, and no dependency on someone else's servers. But knowing why is only half the picture. This guide covers the how — what hardware to buy, what software to install, and what you can realistically expect from a local AI setup in 2026.

We're not theorising here. We run four AI services simultaneously on a single Apple Mac Studio in our office. It handles speech-to-text, text-to-speech, a large language model, and text embeddings — all day, every day. No cloud. No subscriptions. No data leaving the building.

This guide is for business owners who are technically curious but not necessarily developers. You don't need to be a programmer to follow along, but you should be comfortable installing software and poking around in a terminal.

What you actually need: the hardware

The single most important factor for local AI is memory — not storage, not processor speed, but RAM. AI models are large files that need to be loaded entirely into memory to run. A 7-billion-parameter language model needs roughly 4–8 GB of RAM. A 70-billion-parameter model needs 40–70 GB. The more memory you have, the larger (and smarter) the models you can run.

Apple Silicon machines have a particular advantage here. Their unified memory architecture means the CPU and GPU share the same pool of RAM, so the full amount is available for AI inference. A Mac Studio with 128 GB of unified memory can run models that would require a very expensive NVIDIA GPU on a traditional PC.

Here's a practical breakdown of what different hardware levels can do:

Our recommendation for most small businesses is the Mac Studio M4 Max with 128 GB of unified memory. It's quiet, compact, energy-efficient, and powerful enough to run every AI service we use. It sits on a shelf in our office and draws less power than a desk lamp.

The Linux/NVIDIA route gives you more raw horsepower and flexibility, but it requires more technical knowledge to set up and maintain. If you're comfortable with Linux system administration, it's an excellent option. If not, Apple Silicon is the path of least resistance.

The software stack: what to install

Hardware is only half the equation. You need software that can load AI models and expose them as services your other tools can talk to. Here's what we recommend for each of the four core AI capabilities:

Language models (LLM)

LM Studio or Ollama — these are the two easiest ways to run large language models locally. Both give you a simple interface to download models, load them into memory, and interact with them. LM Studio has a graphical interface that feels like ChatGPT. Ollama is command-line-based and better suited for integration with other tools.

Both expose an OpenAI-compatible API, which means any application that works with ChatGPT can be pointed at your local machine instead. No code changes required — just change the URL.

Start with a model like Llama 3 (8B) or Qwen 2.5 (7B) to test the waters. If your hardware supports it, move up to 32B or 70B parameter models for more capable reasoning and longer context windows.

Speech-to-text (STT)

Whisper — OpenAI released this model as open source, and it's genuinely as good as most cloud transcription services. On Apple Silicon, you'll want to run it via MLX Whisper, which is optimised for the Mac's neural engine. On Linux with an NVIDIA GPU, whisper.cpp or faster-whisper are the best options.

We use the large-v3 variant, which handles Australian accents, medical terminology, and background noise remarkably well. On our M4 Max, it transcribes faster than real-time — a 10-minute recording is fully transcribed in under 5 minutes.

Text-to-speech (TTS)

Piper TTS is our pick for local text-to-speech. It's lightweight, fast, and produces natural-sounding voices. There are dozens of pre-trained voice models available, and on an M4 Max, synthesis is near-instantaneous — you feed it a paragraph and get audio back in under a second.

This is what powers voice responses in automated phone systems, voice notifications, and any other scenario where your AI needs to speak rather than type.

Embeddings

All-MiniLM-L6-v2 or a similar sentence transformer model. Embeddings are the least glamorous part of the stack, but they're what make semantic search possible. Instead of keyword matching, embeddings let you search by meaning. Ask for "crown preparation" and it'll find notes about "porcelain restoration" because it understands they're related concepts.

Embedding models are tiny compared to LLMs — this one produces 384-dimensional vectors and uses barely any memory. You can run it alongside everything else without any performance impact.

What you can actually do with this

Once you have these four services running, the practical applications start stacking up:

• Transcribe meetings, calls, and voice notes — drop an audio file in, get a full transcript out. No cloud service, no per-minute charge, no data leaving your network.
• Generate, edit, and summarise text — draft emails, summarise documents, extract key information from reports. The LLM handles all of this locally.
• Build searchable knowledge bases — feed your documents, notes, and records through the embedding model to create a semantic search system. Find what you need by meaning, not just keywords.
• Create voice responses — build automated phone systems, voice notifications, or accessibility features that speak naturally.
• Classify and route information — automatically categorise emails, sort documents, triage requests. The LLM reads and decides; your scripts execute.

All of this runs privately. All of it is free after the initial hardware purchase. And all of it is available 24/7 without depending on anyone else's infrastructure.

Performance: what to expect

Let's set realistic expectations. On our Mac Studio M4 Max with 128 GB of unified memory:

• Whisper (large-v3) transcribes faster than real-time. A 60-minute audio file is done in roughly 30 minutes.
• LLM (32B model) generates 50+ tokens per second. That's faster than you can read. Responses feel instant for short queries.
• TTS is near-instantaneous. A full paragraph of speech is synthesised in well under a second.
• Embeddings process thousands of text chunks per minute. Building an entire knowledge base from scratch takes minutes, not hours.

On entry-level hardware like a Mac Mini M4, expect roughly half these speeds and the need to run services one at a time rather than simultaneously. It's still perfectly usable for a small business — just not as concurrent.

Is this right for you?

Local AI isn't for everyone. Here's a quick decision guide:

For most small businesses handling any kind of sensitive data — healthcare, legal, financial, HR — the privacy argument alone justifies the investment. When you add the cost savings from eliminating per-request API charges, the hardware pays for itself faster than you'd expect. We break down the full economics in our guide to custom AI costs.

Getting started

If you're ready to try this, here's the simplest path:

1. Start with what you have. If you own a recent Mac with 16 GB or more of RAM, download LM Studio and load a small model. You'll have a working local ChatGPT alternative in under ten minutes.
2. Add speech-to-text. Install MLX Whisper (Mac) or faster-whisper (Linux/Windows) and test it with a voice recording. The quality will surprise you.
3. Scale up when you're ready. Once you've seen what local AI can do on modest hardware, invest in a dedicated machine. The Mac Studio M4 Max is our recommendation for the sweet spot of performance, simplicity, and value.

You don't need to build everything at once. Start with one capability, prove it works for your business, and expand from there.

We can help

We've been running this stack in production for over a year. We know what works, what doesn't, and where the sharp edges are. If you want to run AI on your own hardware but aren't sure where to start, get in touch. We'll help you figure out the right setup for your business — no lock-in, no subscriptions, just hardware you own and software that runs on it.