Any idea what makes models hard to run on it?

Just general compatibility between Nvidia and AMD for stuff that was built for Nvidia originally?

Or do you mean something else?

"The AMD Ryzen™ AI Max+ processor is the first (and only) Windows AI PC processor capable of running large language models up to 235 Billion parameters in size. This includes support for popular models such as: Open AI's GPT-OSS 120B and Z.ai Org's GLM 4.5 Air. The large unified memory pool also allows models (up to 128 Billion parameters) to run at their maximum context length (which is a memory intensive feature) - enabling and empowering use cases involving tool-calling, MCP and agentic workflows - all available today. "

  GPT-OSS 120B MXFP4              : up to 44 tk/s
  GPT-OSS 20B MXFP4               : up to 62 tk/s
  Qwen3 235B A22B Thinking Q3 K L : up to 14 tk/s
  Qwen3 Coder 30B A3B Q4 K M      : up to 66 tk/s
  GLM 4.5 Air Q4 K M              : up to 16 tk/s

(performance tk/s ) : https://www.amd.com/en/blogs/2025/amd-ryzen-ai-max-personal-...

Hardware companies are extremely bad at valuing software. The mystery isn't that AMD is bad at it, the mystery is that NVidia is good at it. They also have a probably 30-40 year head start. AMD is trying as much as they can, but changing culture takes time.

> Why does AMD come across as so generally clueless when it comes to giving developers what they want, compared to Nvidia?

I have some theories. Firstly, Nvidia was smart enough to have a unified compute GPU architecture across all its architectures -- consumer and commercial. AMD has this awkward split between CDNA and RDNA. So while AMD is scrambling to get CDNA competitive, RDNA is not getting as much attention as it should. I'm pretty sure its ROCm stack has all kinds of hacks trying to get things working across consumer Radeon devices (which internally are probably not well suited/tuned for compute anyways). AMD is hamstrung by its consumer hardware for now in the AI space.

Secondly, AMD is trying to be "compatible" to Nvidia (via HIP). Sadly this is the same thing that AMD did with Intel in the past. Being compatible is really a bad idea when the market leader (Nvidia) is not interested in standardising and actively pursues optimisations and extensions. AMD will always play catch up.

TL;DR AMD made some bad bets on what the hardware would look like in the future and never thought software was critical like nvidia.

AMD now realizes that software is critical and what future hardware should look like. However it is difficult to catch up with Nvidia, the most valuable company in the world with almost limitless resources to invest in further improving its hardware and software. Even while AMD improves, it will continue to look bad in comparison to Nvidia as state of art keeps getting pushed forward.

One issue is you need rocm 7 which only just came out.

Another is that people unsportingly write things in cuda.

It'll be a "just works" thing eventually, even if you need software from outside AMD to get it running well.

You can run ROCm and PyTorch natively for strix halo on both windows and linux. See https://rocm.docs.amd.com/en/docs-7.9.0/index.html

It’s a gaming chip.

I have a Strix Halo based HP ZBook G1A and it's been pretty easy getting local models to run on it. Training small LLMs on it has been a bit harder but doable as well. Mind you, I 'only' have 64 GB with mine.

Under Linux, getting LM Studio to work using the Vulkan backend was trivial. Llama.cpp was a bit more involved. ROCm worked surprisingly well with Arch — I would credit the package maintainers. The only hard part was sorting out Python packaging for PyTorch (use local packages with system's ROCm).

I wouldn't say it's perfect but it's definitely not as bad as it used to be. I think the biggest downside is the difference in environment when you use this as a dev machine and then run the models on NVIDIA hardware for prod.

I don't know why you're getting downvotes on this, my experience matches it. I have an Evo-X2 which has Strix Halo and rocm still doesn't officially support it. "Support" is supposedly coming in 7.0.2 which can be installed as a preview at the moment but people are still getting regular and random GPU Hang errors with it. I'm running Arch and I've had to make a bunch of tasks in a mise.toml so that I don't forget the long list environment variables to override various rocm settings, and even longer list of arcane incantations required to update rocm and PyTorch to versions that actually almost work with each other.

This article from the same site goes into the Infinity Cache design in a bit more detail: https://chipsandcheese.com/p/amds-cdna-3-compute-architectur...

The summary is that it's a cache attached to the memory controllers, rather than the CPUs, so it doesn't have to worry about cache coherency so much. This could be useful for shared memory parallelism.

What makes Intel's SMT implementation "hyper"? What makes Mario "Super"? It's just marketing.

AMD named their memory fabric "infinity fabric" for marketing reasons. So when they developed their memory attached cache solution (which lives in the memory fabric, unlike a traditional cache), the obvious marketing name is "infinity cache"

The main advantage of a memory attached cache is that it's cheaper than a regular cache, and can even be put on a seperate die, allowing you to have much more of it.

AMDs previous memory fabric from the early 2000s was called "Hyper Transport", which has a confusing overlap with Intel's Hyper Threading, but I think AMD actually bet intel to the name by a few years.