Cloud AI APIs are convenient, but they come with recurring costs, latency, and the small matter of sending your data to someone else's servers. Running a language model on your own machine solves all three — and in 2026 the tooling has matured enough to be genuinely practical on modest hardware.
Why run models locally?
Privacy: your prompts never leave your machine. Cost: after the hardware, inference is essentially free. Offline access: a local model works anywhere. The trade-off is capability on the hardest reasoning tasks, but for code completion, summarisation and drafting, the gap is smaller than many expect.
The tooling: Ollama and LM Studio
Ollama is the simplest start: install it, run ollama run llama3.2, and you're chatting within minutes. LM Studio is the friendlier GUI alternative. If you'd like a fuller walkthrough, this UK tech walkthrough covers the whole setup step by step.
What hardware do you actually need?
The key number is memory: a quantised 7–8B model needs about 5–6 GB of free memory, a 13B model around 8–9 GB, and a 32B model roughly 20 GB. A laptop with 16 GB of unified memory comfortably runs 7–8B models.
Which models suit modest hardware?
The 7–8B class is the sweet spot: Llama 3.2 8B, Mistral 7B, Qwen 2.5 7B. For coding, smaller code-specialised models often outperform larger general ones.
A note on expectations
Local models hallucinate — treat them as drafting assistants, not oracles, and verify anything factual.
Top comments (0)