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Offline AI: The Best Hardware and Models for Running Local LLMs

By Tom Langston ·

  • AI
  • LLM
  • Hardware
  • Privacy
  • Apple Silicon

TL;DR

You can run genuinely capable artificial intelligence (AI) entirely on your own device, offline, with nothing ever sent to a company’s servers. For anyone nervous or sceptical about online AI and where their data goes, this is the answer: the benefits of AI, none of the sending-your-life-to-the-cloud part. The catch is hardware, because local AI is hungry for memory and compute, and the machine you pick matters enormously.

Now for the more technical explanation

Why offline AI genuinely matters

Plenty of people are dubious about online AI, and not unreasonably. Every prompt you type into a hosted service leaves your device and is processed under someone else’s policies. If that gives you pause, the usual response is to avoid AI entirely, which means missing out on a genuinely useful tool. Offline AI is the third option, and it is a good one:

If you have been refusing to touch AI on privacy grounds, learning to run it locally is genuinely worth your time. It removes the objection entirely.

The one thing that decides everything: memory

Before any brand names, understand the single most important factor: memory. A large language model is a big pile of numbers (its parameters), and to run it, that pile has to fit into fast memory, either system RAM (on unified-memory machines) or a GPU’s VRAM. Two rules follow:

This is why “how much memory, and how fast” matters more than almost anything else when picking hardware for local AI.

The platforms

Apple Silicon (the mature king). Apple Silicon Macs are, right now, the most mature and effortless consumer platform for local AI, and the reason is unified memory. The CPU, GPU, and Neural Engine all share one big pool of fast, high-bandwidth memory, so a Mac with plenty of unified RAM can load a large model and let the GPU chew through it at high bandwidth. Combined with a very mature software stack (Apple’s MLX framework and Metal, plus first-class support in the popular tools), it means local AI on a Mac mostly Just Works, quietly, on battery, with no fiddling. In my own testing across Apple Silicon machines, this maturity is the standout: it is the least-hassle path to a genuinely good local model.

Windows on Snapdragon (catching up, still behind at equal spec). Qualcomm’s Snapdragon X laptops also use unified memory and pack a strong neural processor, and they are improving quickly (see the x86 vs ARM guide for the wider efficiency story). But here is the honest finding from testing the two side by side: at the same spec, Apple still clearly outperforms. Take two 16GB unified-memory laptops, one Apple Silicon and one Snapdragon X Elite, and the Apple machine delivers a noticeably better local-AI experience, thanks to higher usable memory bandwidth and a more mature software stack. Snapdragon is a promising, improving platform, and the gap is closing, but as of now it is not at parity with Apple for this specific job. Worth knowing before you buy an ARM Windows laptop for local AI.

Desktop with a discrete NVIDIA GPU (raw power). If portability is not the point, a desktop with a big NVIDIA graphics card is the raw-performance champion, and here VRAM is everything:

NVIDIA is the default because CUDA is the mature ecosystem with the best software support. AMD (via ROCm) works and is improving, but the tooling is fiddlier; Intel Arc is niche. For a dedicated offline-AI box, a 16 to 24GB NVIDIA card is the sweet spot. The trade-off is obvious: a big-GPU desktop is fastest but power-hungry, hot, and stuck on your desk, which is exactly why Apple Silicon laptops are so beloved, they deliver a lot of that capability silently and on battery.

What hardware do you need to run AI locally?

The guiding principle is simple: buy memory. It is the ceiling on what you can run.

Then the software: Ollama is the easiest way in (one command to pull and run a model), LM Studio gives you a friendly GUI, and both sit on top of the same engines. Use quantised versions of models (compressed to Q4 or Q5) so a bigger model fits in less memory with little quality loss.

The models, and the fuzzy truth about “how good is it vs ChatGPT?”

The open models worth knowing come in families, Gemma (Google), Qwen (Alibaba), Llama (Meta), Mistral, and Phi (Microsoft), each in a range of sizes measured in billions of parameters: small (1 to 4B), mid (7 to 9B), and large (13 to 70B and beyond).

Now the question everyone asks: how good is a local model compared with the free tier of ChatGPT, Claude, or Gemini? The honest answer is that it depends, wildly, and anyone quoting you a hard percentage is guessing, because the result hinges on the exact model, its quantisation, your hardware, the task, and frankly your own expectations. With that caveat blaring:

Treat every one of those numbers as a vibe, not a benchmark.

A worked example (with every caveat still applying)

To make it concrete: in my testing, something like a MacBook Air with 24GB of unified memory and a current base M-series chip (an M4 or M5, not the pricier Pro or Max) running a good 7-to-9-billion-parameter model gives a genuinely pleasant, useful experience, fast enough to feel conversational. For a lot of everyday work it lands in the rough ballpark of a free online assistant, call it roughly 80 to 90 per cent of that experience, with the loud reminder that this is subjective and task-dependent. Not identical, not for everything, but close enough that a privacy-conscious person could comfortably use it as their daily AI and never send a word to the cloud. That, for a lot of people, is the whole point.

The power and battery reality (AI is hungry)

One thing worth setting expectations on: AI is heavy work, and it shows in power draw. Modern efficient laptops are astonishingly optimised for light tasks, but local AI is not a light task. To put it in rough, illustrative terms from my own testing: an efficient Apple Silicon laptop that will happily play video for the better part of a day (think the high teens to twenty-ish hours) can drain in a handful of hours if you pin it running a local model flat out.

Two things to take from that. First, you almost never do run a model continuously for hours, inference is bursty (you ask, it answers in seconds, then the machine idles), so real-world battery impact is far gentler than “five hours flat out” suggests. Second, it is a good reminder that these machines are beautifully optimised for everyday work, and that AI genuinely demands a lot of compute, which is exactly why memory and the efficiency of the whole platform matter so much. (And, again, these numbers are illustrative, not spec sheets; hardware, model, and settings all move them around.)

The short version

Offline AI lets you run a genuinely useful assistant entirely on your own hardware, private, subscription-free, and offline, which is the perfect answer if you are wary of online AI. The hardware that matters is memory: how much, and how fast. Apple Silicon is the mature, effortless leader thanks to unified memory; Snapdragon is catching up but still trails Apple at the same spec; a big NVIDIA GPU desktop is the raw-power option. A modern 7-to-9-billion-parameter open model on something like a 24GB Apple Silicon laptop gets you a rough 80 to 90 per cent of a free online assistant for everyday tasks, with every number here being a fuzzy, hardware-and-expectation-dependent estimate rather than a promise. Learn to run it, and you get AI on entirely your own terms, an assistant that is still there when the internet, or your usage allowance, is not. Just remember that a local model fixes availability, not your own competence: keep learning the jobs you delegate to it so you are never a sitting duck the day any AI, online or offline, lets you down.

Written by Tom Langston, IT Infrastructure and Cybersecurity.