Buying a “local AI PC” isn’t about getting the newest CPU. It’s about building a machine where your models fit, your responses are fast, and your total cost stays predictable.
Quick truth: VRAM determines what you can run, memory bandwidth determines how fast it feels, and context length determines whether RAG is useful or disappointing.
For local LLMs, VRAM is the limiting factor. Even quantized models can be large: a 70B model in 4-bit quant can land around the 35–43GB range, and long contexts add KV cache overhead.
In interactive chat, bandwidth often matters more than raw compute. 2025 GPUs have made major gains here, and that’s why newer cards feel dramatically snappier even at similar VRAM capacity.
Local stacks are a model library. NVMe keeps your workflow tight when you’re switching between multiple models and embedding stores.
If you need to run and experiment with larger models, do RAG with big documents, and keep latency low, you want a workstation-class system.
If you want a compact machine that can live in an office, travel, or sit on a desk without sounding like a jet engine, modern mini workstations are the move.
If multiple people will use the same private AI, you should plan for concurrency. That means stable serving, batching, and a reliable UI (Open WebUI or similar) with permissions.
In our stack, that often means an on-prem inference box + a clean internal chat UI.
Every Project Infra computer is designed for “sovereign AI” — meaning your tools and data stay in your environment. Typical baseline stack:
Want a machine that’s already tuned?
Browse Project Infra computers and pick your lane. If you’re unsure, we’ll spec it for your use case.