New Framework Desktop Option with AMD Ryzen AI Max+ Pro 495 and 192GB Memory

I don't see anything wrong with the name.

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.

The next big step is Medusa Halo, which will have a 384-bit LPDDR6 interface. Those should be able to support 256GB at release, with 512GB coming later with larger chips (But I don't know to what extent people should trust the memory vendor roadmaps.) I'm not sure if they will be out in a year. Probably will be in a year and half.

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).

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.

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

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.

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.

It’s fully possible that I’ve missed something, but it doesn’t appear to be proprietary to me.

[1]: https://frame.work/desktop?tab=specs

[2]: https://frame.work/products/desktop-case

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.

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.

https://www.phoronix.com/news/AMD-Lemonade-11.5

https://lemonade-server.ai/

What would you say is the likelihood of an LLM hallucination vs. a correctable RAM error?