Framework Desktop, 32GB vs 64GB for local AI
The Framework Desktop, 64GB holds 34 of the 50 current models here, and the Framework Desktop, 32GB holds 30. The Framework Desktop, 32GB costs $690 less. On Qwen3.8 27B, the strongest model both hold, they run at much the same speed: 10 and 10 tok/s, both estimated from memory bandwidth. The Framework Desktop, 32GB pays for itself sooner, in 91 years against 140 years at 500k tokens a day.
| Strix Halo Framework Desktop, 32GB | Strix Halo Framework Desktop, 64GB | |
|---|---|---|
| Price | $1,269 | $1,959 |
| Memory | 32 GB | 64 GB |
| Usable by the GPU | 24 GB | 48 GB |
| Memory bandwidth | 256 GB/s | 256 GB/s |
| Power under load | 133 Wstand-in | 133 W |
| Models that fit | 30 | 34 |
| Best model it runs | Qwen3.8 27B | Qwen3.8 27B |
| Speed on that model | 10 tok/s estimated | 10 tok/s estimated |
| Pay-back on that model | Pays back in 91 years | Pays back in 140 years |
The 133 W beside the Framework Desktop, 32GB is a stand-in, not a figure for that machine: the data borrows it from the nearest hardware it does have, and the machine's own page names which and why. So the two figures above are not like for like, and their matching says nothing about either machine, and the electricity in its pay-back here is priced from a borrowed number.
Run the numbers on the Strix Halo Framework Desktop, 32GB · or the Strix Halo Framework Desktop, 64GB
Jump to: The step up is a different chip, not just more memory · How much use it takes to pay back · What the extra memory buys · The assumptions behind both columns
The step up is a different chip, not just more memory
Framework Desktop is one name for two machines. The data lists the 32GB as Ryzen AI Max 385 · Radeon 8050S, 32 CU and the 64GB as Ryzen AI Max+ 395 · Radeon 8060S, 40 CU. The step adds 8 compute units to the graphics part, 40 against 32. That is why this pair is not one of the site's memory-size comparisons: those hold the silicon equal so the memory is the whole of the difference, and Framework cut both to reach the $1,269.
The bigger graphics part is not what sets the speeds on this page. Writing a token means reading the whole model out of memory, and both chips read it at 256 GB/s, so the figures follow the memory rather than the chip. That is why the table gives them the same speed. What the extra compute units do is read a long prompt before the first token comes back, and that is not something this site measures or prices. The 64GB box holds 34 of the 50 models the calculator counts against 30 on the 32GB. Price the Framework Desktop, 32GB on its own before paying for the step: if the models you want fit the cheaper box, the chip above it is not buying you tokens.
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 Strix Halo Framework Desktop, 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, 32GB | Strix Halo Framework Desktop, 64GB |
|---|---|---|
| 50ka few chats a day | 907 years | 1,400 years |
| 200klight assistant use | 227 years | 350 years |
| 1Ma moderate coding-assistant day | 45 years | 70 years |
| 4Mheavy coding with an agent | 11 years | 17 years |
| 20Magents running most of the day | 3.2 yearsits ceiling | 4.9 yearsits ceiling |
On Qwen3.8 27B neither machine can generate 20M tokens a day: the Strix Halo Framework Desktop, 32GB manages at most 14.3M and the Strix Halo Framework Desktop, 64GB at most 14.3M. Both figures on that row are for the most each can do.
Run the Strix Halo Framework Desktop, 32GB at 20M tokens a day · or the Strix Halo Framework Desktop, 64GB
What the extra memory buys
The Strix Halo Framework Desktop, 64GB holds 4 models the Strix Halo Framework Desktop, 32GB cannot at 32k of context. The strongest of them are what the difference in memory actually buys.
| Model | Weights | Needs at 32k | On the Strix Halo Framework Desktop, 64GB |
|---|---|---|---|
| Granite 4.2 30BHaiku-class | 18 GB | 26 GB | 7.3 tok/s estimated |
| Nemotron 3.5 Lightning 30B-A3BBelow every hosted tier | 25 GB | 26 GB | 54 tok/s estimated |
| Flint 40B-A2.5Bnot yet placed | 25 GB | 26 GB | 55 tok/s estimated |
| Nanbeige4.3 33Bnot yet placed | 21 GB | 28 GB | 6.9 tok/s estimated |
The extra memory buys context as well. Of the 30 current models both machines hold at 32k, 14 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 32k on the Strix Halo Framework Desktop, 32GB, each the longest window the calculator offers that the machine still holds it at.
The Framework Desktop, 32GB is also head to head with another computer: Mac mini M6, 32GB.
The Framework Desktop, 64GB is also head to head with another computer: GMKtec EVO-X2, 64GB. With a graphics card: GeForce RTX 5090, 32GB. With the same machine at another memory size: 128GB.
Each size has a page of its own, listing every machine here sold at it and the models that fit: what 32 GB runs · what 64 GB runs. The 7 machines here still sold at 64 GB start with the Corsair AI Workstation 300, 64GB at $1,700, $259 below the Framework Desktop, 64GB. Both read memory at 256 GB/s.
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. Change any of it in the calculator.
More head to head: everything the Strix Halo Framework Desktop, 32GB runs · everything the Strix Halo Framework Desktop, 64GB runs · every other match-up · the quickest pay-back at each level of use · every model against the frontier