How does a CEO read this and not immediately fire their entire marketing team?
I don't see anything wrong with the name.
Does anyone have a sense of how this might progress, e.g if I can get a 256 or 512 GB in a year if I wait. In any case I’m jealous this exists and I don’t have one.
One last thing, I assume this isn’t exclusive and there will be other builds with this same config same as current Strix Halo?
They are estimating a 25% shortfall in supply remaining in 2030, even with Chinese companies ramping up DDR5 supply.
It's not looking great - the RAM producers need more of the same machines that other semi-conductor manufactures need and the suppliers of those seem unable to increase production.
I suspect that it is very, very profitable for them not to increase production and instead increase prices.
This is one of those cases where the free market fails and governments should step-in. IP laws are there to elevate the arts and sciences, not stifle them in the name of profit.
I'm kinda tempted by the frameworks because they are somewhat energy efficient and compact (which I both value highly!) but my concern is that the GPU is on the low end for 1440p gaming (while being barely price-competitive with a self-built Ryzen9950 + 9070XT combination).
Cyberpunk ray tracing is a bad experience, but I just finished 007 First Light on ultra settings with an 5k monitor using FSR and it was 45+ fps the whole time. Targeting 4K with fsr was 60fps.
I’m not a hardcore gamer or a pro reviewer, but it mostly just works without having to change much. If I were a hardcore gamer I think I would go with a better setup.
My big complaint for gaming is that they never enabled HDMI CEC. Apparently the pins are wired but they never shipped the firmware.
I agree, this is the direction AMD was going in to larger attached memory and they got hit by the memory cartel pricing; They likely were going to hit 256GB instead of this weird glitch in the sizing.
So, yes, of course they're going to hit higher memory sizes; but since they models are meant for laptops and to get the speed you want for inference, they're soldered, you have no real options.
Framework in particular might have a high value on ebay as I assume this will be a drop in replacement for their existing motherboard.
Dear people who create websites, these things are important, they should work!
This is a simple form, not a complicated thing, and it's the website of a company I love, so I'm paying extra attention perhaps.
Technically it's not wrong to say it's about performance in the sense that if you clocked the RAM slow enough you probably could maintain signal integrity, but we're not talking some small hit to performance here, it'd probably be more like a multi-generational drop in memory bandwidth.
It’s fully possible that I’ve missed something, but it doesn’t appear to be proprietary to me.
The whole point is that these APUs present a pretty unique value proposition (lowish performance GPUs with massive amounts of RAM attached), and framework is afaik the only vendor shipping them on standard mini-ITX boards
I've had best results with Qwen3.6-35B-A3B, which uses 40GB of memory, but only uses 3 billion parameters per token which helps with throughput.
Until memory bandwidth significantly improves I just can't see myself wanting to use all that memory. Unless it's just to keep a wide variety of models in memory.
If your performance is significantly slower then you are probably doing it in CPU - there was some fiddling required to get it to use GPU (I use llama.cpp)
or Qwen 3.5 122B A10B, both use more memory and still have experts sized for decent speed at the 395’s memory bandwidth at 4bit quantization
But I actually think 128GB is too little. There are some compelling models that are above what can fit in that at reasonable quants (e.g. DeepSeek V4 Flash) but could if the system was 256GB.
If RAM prices weren't so f*cked I think we'd be seeing 256GB and even 512GB unified memory systems becoming quite common. As it is I think it will be 10 years before >128GB becomes feasible on a regular consumer machine for normal people again.
Not sure what damage you have, but no one waits 40 minutes per prompt or even a minute; The A3B model loads within 10 seconds and a simple response in opencode is maybe at most a minute, then catches up in 3-5second bursts depending on IO.
I put dynamic context pruning into opencode and tweaked it for 45k-85k context before it shrinks; this lets me get into 500k token sizes and fixed on medium sized github repos.
There's a chance this comment is a skill error:
1. USe llamacpp with a MTP model
2. Use reasoning-budget and reasoning-message
3. Tailor your agent to use the reasoning-message to use subagents and dyanmic compaction.
---
Now you have a reasonable coding agent for cloning, building and extending any github project I've seen so far.
Anyone who thinks they are going to serve some 100+ GB LLM locally, remember that memory bandwidth becomes a key limitation for large models. While you might be able to load a model, token generation can be very slow. MoE models like Qwen3.5-122B-A10B work decently fast, but dense models of a decent size are slow and you won’t want to use them.
I’ve got a 395 system, and found that I’m quite happy with Qwen3.6-35B-A3B, generating at around 50 t/s, but the dense 27B model is 20-25 t/s and that’s the lower limit I’m willing to tolerate. So a 70b dense model is just not going to happen. That means you can’t really use that much RAM.
A reasonable use case is to have multiple smaller models loaded- you can have an image generation model loaded along with the text model. Or you can use this computer for development simultaneously with serving LLMs. Those ideas work okay. But trying to load up a single giant model is going to test your patience.
Abd wake up without crashing?
Feels like they blatantly ripped that off the title of a Cory Doctorow book [0].
And into the trash it goes.
> 192GB coming soon
> The most powerful Framework Desktop yet is coming soon with an AMD Ryzen™ AI Max+ PRO 495 processor and 192GB of LPDDR5X memory.