Running local LLMs in 2026 - is your hardware good enough now

[Lucky Dean]

Ollama, LM Studio and similar tools have made running local models genuinely accessible. In 2026 you can run Llama 3.3 70B quantised on a 24GB VRAM card, Mistral and Phi-4 on mid-range consumer hardware, and smaller models comfortably on integrated NPUs on newer laptops. For anyone handling sensitive data or just wanting offline access, the local model path has never been more viable

[Arty Kayla]

RTX 5090 with 32GB VRAM is basically a local inference machine at this point. Running 70B at Q4 is fast enough for real work

[Blake_73]

Phi-4 on an NPU laptop is actually usable for everyday tasks. The quality gap from the big cloud models is still there but it is smaller than you would expect

[Rachel]

The main reason I run local is client data. Zero API calls means zero legal risk. No cloud model is good enough to make me change that policy

[Amy96]

LM Studio made this genuinely accessible. You do not need to touch a command line if you do not want to

[Shane_8]

Quantisation quality matters more than people admit. A Q2 version of a 70B model is often worse than a Q8 version of a 13B. Benchmark before you commit

[Mia86]

Apple Silicon is still the best value for memory bandwidth relative to cost. The M4 Max with 128GB unified memory runs huge models faster than most people expect