Strix Halo Framework Desktop, 64GB vs NVIDIA GeForce RTX 5090, 32GB for local AI

Both hold 27 of the 39 open models here. The Framework Desktop, 64GB costs $40 less, though the GeForce RTX 5090, 32GB is priced as the card alone, without the PC around it. On Qwen3.8 27B, the strongest model both hold, the GeForce RTX 5090, 32GB is about 6.6× faster: 66 tok/s against 10, both estimated from memory bandwidth. The GeForce RTX 5090, 32GB pays for itself sooner, in 25 years against 26 years at 500k tokens a day.

Run the numbers on the Strix Halo Framework Desktop, 64GB · or the NVIDIA GeForce RTX 5090, 32GB

The same money, two different machines

These two cost within $40 of each other: $1,959 for the Framework Desktop, 64GB and $1,999 for the GeForce RTX 5090, 32GB, card only. Framework makes one and NVIDIA the other. Every other head-to-head on this site holds a piece of the hardware equal and asks what the price gap buys. This one holds the price, so the row that usually carries the answer is the row the two machines agree on, and everything under it is what the same money buys twice. The two are less equal than they look: the GeForce RTX 5090, 32GB's price buys the card alone, so read its column as the cost of the part that does the work on top of a machine you already own.

How much use it takes to pay back

Everything above is at 500k tokens a day. Pay-back moves with how much you actually run, so here are both machines on Qwen3.8 27B, the strongest model both hold, at the five levels of use the calculator names. The NVIDIA GeForce RTX 5090, 32GB pays back sooner at every level of use, so this is not a choice that turns on how hard you work it.

On Qwen3.8 27B the Strix Halo Framework Desktop, 64GB generates at most 14.3M tokens a day, so its figure at 20M tokens a day is for the most it can do, not for the whole of what was asked.

Run the Strix Halo Framework Desktop, 64GB at 20M tokens a day · or the NVIDIA GeForce RTX 5090, 32GB

The same models, not to the same length

Every model on this list that fits one machine fits the other at 32k of context, so memory does not change what they run. What it changes is how far you can take the context on 8 of them. The Strix Halo Framework Desktop, 64GB has 48 GB usable against 31 GB, and spare memory is what the KV cache grows into as you keep more tokens.

Each figure is the longest context the calculator offers that the machine still holds that model at, and no model is taken past its own context limit. The other 31 models the calculator counts reach the same length on both machines, at every setting from 4k to 256k.

Run Qwen3.8 27B on the Strix Halo Framework Desktop, 64GB at 256k · or on the NVIDIA GeForce RTX 5090, 32GB at 128k

The Framework Desktop, 64GB is also head to head with another computer: GMKtec EVO-X2, 64GB. With the same machine at another memory size: 128GB. With the same box and the smaller chip: 32GB.

The GeForce RTX 5090, 32GB is also head to head with another card: RTX PRO 6000 Blackwell, 96GB · GeForce RTX 4090, 24GB · GeForce RTX 3090, 24GB · Radeon AI PRO R9700, 32GB · GeForce RTX 4080, 16GB · GeForce RTX 3060, 12GB. With a complete computer: MacBook Pro M5 (14-inch), 16GB.

The assumptions behind both columns

Both columns use the same usage: 500k tokens a day at 15:1 input to output, 32k of context, $0.17 per kWh, and today's API prices held flat. Speeds marked estimated are worked out from memory bandwidth rather than measured, and pay-back scales with them. The GeForce RTX 5090, 32GB is priced as the card alone, so add the PC around it before comparing it with a complete computer. All seven cards here are ranked by what each one holds. Change any of it in the calculator.

More head to head: everything the Strix Halo Framework Desktop, 64GB runs · everything the NVIDIA GeForce RTX 5090, 32GB runs · every other match-up · the quickest pay-back at each level of use · every model against the frontier