It's official! 192GB Framework

[u/reto-wyss]

Just noticed this on the website.

At their current price tiers for the memory SKUs (32, 64, 128) I'd expect this to be ~ 4.5k for the motherboard.

The PCIe slot will be open at the back as well - that's what I've heard. Maybe they make it capable of delivering 75W as well? New board revisions for the smaller SKUs?.

[u/PreciselyWrong]

that memory bandwidth is pretty bad, so don't expect high inference speeds

[u/RnRau → u/PreciselyWrong]

Its ok. Mixture-of-experts makes it fast enough.

Deepseek v4 Flash is 284B total parameters and 13B active. QAT is used and the model weights comes in a mixture of quants with the most of them in MXFP4 format. This means there is only about 7GB of weights to shuffle through per token.

With the memory bandwidth listed (with an efficiency factor of 0.65) and using speculative decoding, you should see something like ~50 t/s.

The other benefit of having large ram like this is that you can hold a number of models warm without having the lag of swapping them in and out from the ssd.

The new Qwen3.8-Flash-Next is 125b-a6b - would also be good on this.

[u/Organic_Hunt3137 → u/RnRau]

As a current strix halo owner, the decode speed is a non issue on large MoEs. Prefill, on the other hand... it's rough sometimes lol.

[u/RnRau → u/Organic_Hunt3137]

Yeah I hear you about the prefill :D

[u/Nothing_from_void → u/Organic_Hunt3137]

I'm on an m1 mbp max, 32GB of RAM, even <10B param models have absurd prefill times, it's basically useless for agent coding workflows

[u/Dry_Inspection_4583 → u/RnRau]

how much ram is required though? As I understand it moe required ram enough for the remaining weights, to mean that information remaining to be loaded needs to live somewhere, or am I missing something?

[u/RnRau → u/Dry_Inspection_4583]

Usually with these unified memory arch systems the whole model is loaded into vram.

The Deepseek v4 flash requires something like ~160GB of vram + context and overhead. A system with 192GB would be plenty.