Building a PC for local AI inference in 2026, what actually matters in the spec - good or bad

[Rogue Sam]

Q: I want to run local AI models. What should I prioritise in a build?

A: VRAM is the single most important number. A model has to fit in GPU memory to run at useful speed. 24GB gets you most 13 to 30 billion parameter models quantised to 4 or 8 bit. The RTX 4090 with 24GB is still the consumer king but costs significantly. The RTX 5090 with 32GB has landed but at eye watering prices. The alternative is Apple Silicon. An M4 Mac Mini with 64GB unified memory handles 70 billion parameter models at 4 bit quantisation reasonably and costs less than a single RTX 4090.

Q: What about CPU and RAM?

A: Secondary to VRAM but not irrelevant. Fast RAM matters for CPU inference fallback when VRAM is full. NVMe storage speed matters for model loading time. CPU generation matters less than you might expect for pure inference

[RomanReigns02]

The Apple Silicon recommendation is the one most PC builders overlook. The unified memory architecture means you are not splitting between system RAM and VRAM and the efficiency per watt is remarkable

[WearyCoder]

For Windows builders the 3090 with 24GB is still available second hand at significantly below 4090 pricing and the VRAM is identical. Worth considering if budget is a constraint

[TheLegendBrett88]

The 3090 thermal design runs hot and the PCIe 4.0 bandwidth is the bottleneck in some inference scenarios. Still a good call for the price though

[VoidSentinel74]

Two 4090s in a workstation via NVLink is the sweet spot for serious local inference work. 48GB effective VRAM handles 70B models at decent quality

[Jackson79]

NVLink on 4090 consumer cards is not officially supported. People do it but you need to know what you are getting into

[Oscar_57]

The Ollama software stack makes running local models accessible enough that hardware choice is now the main constraint. The software side has caught up

[Marcus]

Model quantisation formats matter as much as raw VRAM. GGUF at Q4_K_M is the standard tradeoff between quality and size. Some models have better quantisations than others

[Anvil33]

What about dual socket CPU builds with large RAM for CPU only inference. Anyone doing this

[Jonathan_Repetto]

CPU inference is usable for smaller models. Llama 3.1 8B runs at conversational speed on a modern CPU with 64GB RAM. Not fast but functional for certain workflows