An opinionated buyer's guide for teams considering on-prem AI deployments in 2026. Hardware tiers (Mac Studio, DGX Spark, Dell GB300), open-weight model picks per workload, the software stack, a workload decision matrix, a worked example, and the operational reality of month four.
In 2024, running AI locally meant running last year’s model and accepting the productivity gap. By mid-2026 that calculus has changed. Open-weight models — Llama 4 Maverick, Qwen 3 235B, DeepSeek V4 — match closed-API frontier on most production workloads. Hardware capable of running them is now affordable: a Mac Studio M3 Ultra at $5,500 handles 70B-class models comfortably; NVIDIA’s DGX Spark at $4,699 fits 200B-class inference under a desk; Dell’s Pro Max with GB300 puts 748GB of coherent memory and 20 petaFLOPS in a desk-side machine that replaces what used to be a small server room.
This guide is the working framework we use when scoping local-AI deployments for clients. It’s opinionated about which workloads benefit from local deployment, which don’t, what hardware to buy at each tier, what models to run on it, and what operational reality looks like in month four. It includes a workload decision matrix, a worked example walking through a research lab’s decision, hardware quick-reference tables, and model recommendations by workload type. Verified as of mid-2026 and intended to be re-verified quarterly — the open-weight landscape moves fast.
In the guide:
Research labs, healthcare and government teams, sovereignty-focused enterprises, solo operators, and personal AI builders considering whether to deploy AI on hardware they own. Useful as a procurement worksheet or as a structured way to evaluate the local-vs-cloud question.
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