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.
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.
Abd wake up without crashing?
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.
Lemonade server updated yesterday for an Arena model / ai routing.
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The Lemonade local AI server allows easily deploying LLM workloads across Ryzen AI NPUs, Radeon GPUs, and CPUs with ease under both Windows and Linux. The most significant change with Lemonade 11.5 is the completion of the Lemonade Router that can be used for automatically routing queries to relevant models based on defined policies -- or even LLM-as-a-router for using a small LLM to in turn determine which model to route a particular request.
The Lemonade Router allows steering requests based on rule, classifier, semantic similarity, or LLM-as-router policies. The policies are defined in JSON files or can also be authored via the Lemonade GUI. More details on these router capabilities can be found via this commit.
Lemonade 11.5 also introduces a server-side job engine to let clients post multi-step recipes and managing them via /jobs endpoints. From these endpoints the muilti-step recipes can be paused / interrupted / resumed / deleted. And another one is that the lemond daemon can now act as an MCP client host to connect external stdio MCP servers.
Feels like they blatantly ripped that off the title of a Cory Doctorow book [0].
And into the trash it goes.
Wait that. It does, and that's the point. Although the CPU and motherboard do of course also have to support it, but with AMD it is possible. I do it on a 9800x3d.
One bit errors are always both detected and corrected. Multibit errors are mostly at least detected.
What would you say is the likelihood of an LLM hallucination vs. a correctable RAM error?
This is about the latter, and not just the correctable ones; Detecting them also has worth.
> 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.
It’s fully possible that I’ve missed something, but it doesn’t appear to be proprietary to me.
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.
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 like the repairability and modularity of my Framework 13 laptop, and I still bought a maxed out MBP M5 Max because for local LLM, unified memory is all that matters.
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.
I agree but unfortunately that speaks to the depth of demand right now. Even as more capacity comes online (if it does) I suspect it will all get used up without prices needing to drop. Hopefully on the other side of this 10 years from now it means RAM will be insanely abundant and we’ll all have machines with a couple TB as the same inflation adjusted price as say 16GB a few years ago. Considering single digit MB of RAM was the norm in the early-mid ‘90s its not so far fetched.
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.
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Now you have a reasonable coding agent for cloning, building and extending any github project I've seen so far.
Hmmmmmmmmm
I use Lemonade and have only the ROCm installed through it (ETA: their llama.cpp version is a fork, not sure what is different vs the main trunk).
It has all the backends you want. Just a script rebuilds it. Then I just put it in a docker compose and whenever we want to update it, we pull the repo and rebuild it.
I just a models.ini like: version = 1 ``` [*] n-gpu-layers = 999 threads = 28 flash-attn = on mlock = off mmap = off fit = off warmup = on jinja = true direct-io = on cache-prompt = true cache-reuse = 256 models-max = 1 models-autoload = 1 timeout = 600 ctx-checkpoints = 256 cache-ram = 0 mlock = on kv-unified = on no-context-shift = on swa-full = on
[Q36-35B-A3B] model = Qwen3.6-35B-A3B-UD-Q8_K_XL.gguf mmproj = mmproj-BF16.gguf rope-scaling = yarn rope-scale = 1.14441 yarn-orig-ctx = 262144 override-kv = qwen35.context_length=int:1000000 ctx-size = 300000 reasoning = auto reasoning-budget = 421 reasoning-budget-message = you're reasoning too much; compress context and use subagents to investigate further spec-type = draft-mtp spec-draft-n-max = 3 batch-size = 16001 ubatch-size = 16001 cache-idle-slots = on temp = 0.6 top-p = 0.95 min-p = 0.00 top-k = 20 repeat-penalty = 1.1 presence-penalty = 0.0 ```
Using subagents, you keep the context size from bloating, and add tooling for the context compaction and it can keep looping. You probably want to clean up opencode's agent; back in the "Prompt Engineering" days there's a lot of unnecessary fluff. The goal of a prompt should be to get the model in the correct starting place, and not, try to give it everything it ever needs to do what you want it to do.
Nobody knows that pro and max mean specific things.
If AMD needs to create a full cross-product of their hardware feature set, because they intend to have a SKU for every possible combination, they probably need a better scheme.
Say what you want about Celeron/Pentium/i3/i5/i7, but it mostly got the idea across (except on the occasions Intel hijacked it to upbrand SKUs).