Strix Halo Framework Desktop, 64GB vs NVIDIA GeForce RTX 5090, 32GB for local AI
The Framework Desktop, 64GB holds 38 of the 55 current models here, and the GeForce RTX 5090, 32GB holds 37. 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 123 years against 140 years at 500k tokens a day.
| Strix Halo Framework Desktop, 64GB | NVIDIA GeForce RTX 5090, 32GB | |
|---|---|---|
| Price | $1,959 | $1,999card only |
| Memory | 64 GB | 32 GB |
| Usable by the GPU | 48 GB | 31 GB |
| Memory bandwidth | 256 GB/s | 1792 GB/s |
| Power under load | 133 W | 575 W |
| Models that fit | 38 | 37 |
| Best model it runs | Qwen3.8 27B | Qwen3.8 27B |
| Speed on that model | 10 tok/s estimated | 66 tok/s estimated |
| Pay-back on that model | Pays back in 140 years | Pays back in 123 years |
Run the numbers on the Strix Halo Framework Desktop, 64GB · or the NVIDIA GeForce RTX 5090, 32GB
Jump to: The same money, two different machines · How much use it takes to pay back · What the extra memory buys · The assumptions behind both columns
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.
| A day's use | Strix Halo Framework Desktop, 64GB | NVIDIA GeForce RTX 5090, 32GB |
|---|---|---|
| 50ka few chats a day | 1,400 years | 1,228 years |
| 200klight assistant use | 350 years | 307 years |
| 1Ma moderate coding-assistant day | 70 years | 61 years |
| 4Mheavy coding with an agent | 17 years | 15 years |
| 20Magents running most of the day | 4.9 yearsits ceiling | 3.1 years |
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
What the extra memory buys
The Strix Halo Framework Desktop, 64GB holds 1 model the NVIDIA GeForce RTX 5090, 32GB cannot at 32k of context. That is what the difference in memory actually buys.
| Model | Weights | Needs at 32k | On the Strix Halo Framework Desktop, 64GB |
|---|---|---|---|
| Ripple 65B Previewnot yet placed | 39 GB | 41 GB | 4.7 tok/s estimated |
The extra memory buys context as well. Of the 37 current models both machines hold at 32k, 12 run to a longer window on the Strix Halo Framework Desktop, 64GB: the weights are a fixed size and the KV cache is not, so what the weights leave spare is what a longer context grows into. Gemma 4 31B it reaches 256k there against 64k on the NVIDIA GeForce RTX 5090, 32GB, each the longest window the calculator offers that the machine still holds it at.
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