Own the compute

Local AI hardware, chosen by the bottleneck

Memory decides what fits. Bandwidth decides how fast it runs. Price decides whether owning beats renting. This is a short list of genuinely different choices, not a catalog of every box with an “AI” sticker.

Hardware prices checked 2026-07-09 · provider-neutral capacity math
01 / Capacity

Large model, patient user

Ryzen AI Max trades discrete-GPU speed for a much larger unified memory pool. It is the capacity play.

02 / Throughput

Smaller model, faster tokens

RTX 5090 has only 32GB, but roughly 7× the memory bandwidth. It is the speed-per-dollar play.

03 / Both

Production workstation

RTX PRO 6000 combines 96GB and high bandwidth, at a price that needs sustained use to justify.

The short list

HardwareAI-usable memoryBandwidthBest fitCurrent price
GMKtec EVO-X2 (128GB unified)96 GB256 GB/sThe $/GB winner — runs 70B-class and quantized big-MoE (V4 Flash tier) that no consumer GPU fitsfrom $2000
NVIDIA RTX 5090 (32GB)32 GB1.79 TB/sBandwidth-per-dollar king for the A3B-MoE and 27-32B dense workhorsescheck retailer →
NVIDIA RTX PRO 6000 Blackwell (96GB)96 GB1.79 TB/sThe startup standard — only sub-$10K single card that runs 70B+ comfortablycheck retailer →
Honest limitation: capacity is not throughput. The Ryzen AI Max memory bus is 256 GB/s; current high-end discrete GPUs are around 1.8 TB/s. A model fitting locally does not mean it will match a cloud GPU's response speed. Every recommendation here keeps those two questions separate.

Hardware links may be affiliate links. Rankings and fit assessments are editorial; commission never changes the capacity math. Full disclosure.