Framework Desktop, 32GB vs 64GB for local AI

The Framework Desktop, 64GB holds 27 of the 39 open models here, and the Framework Desktop, 32GB holds 24. 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 17 years against 26 years at 500k tokens a day.

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

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 27 of the 39 models the calculator counts against 24 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.

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 3 models the Strix Halo Framework Desktop, 32GB cannot at 32k of context. The strongest of them are what the difference in memory actually buys.

The extra memory buys context as well. Of the 24 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. Qwen3-Coder 30B-A3B 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 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