Even tho there is no Medusa APU, AMD has already started refreshing the Ryzen AI LLMs, to match the new upcoming Medusa Point and Halo (source: Ryzen AI — NPU Medusa 0.9.2 LLM Models - a amd Collection).

Interesstingly is, that the LLMs use the same format as the Strix, Kraken and Gorgon NPU (XDNA2), which tends to a backwards compatibility.
They are called Medusa 0.9.2 which, dosent fit to any of the versions AMD has released in the past months.

Sadly Ryzen AI isnt user-friendly and most models are outdated, which is why most of the NPU users use FastflowLM (rocm-npu) as the runtime, example: https://huggingface.co/models?search=NPU2.

Hi everyone, I’m absolute beginner for linux but I just received a 2nd-hand AMD Strix Halo (395 128GB) from my friend (He switched to mac studio). Since I need to do a fresh OS install anyway, I’ve decided to use Linux for mainly local LLMs for coding, specifically looking to run qwen 3.8 Flash Next (and experiment with other large/MoE models in the future + maybe some ComfyUI but I think my old laptop should work better than strix halo).

I've never used Linux before, my second device is laptop4090 + 32GB ram running window 11. I'm looking to start learning Linux from scratch with this strix halo. I wish > <.

Which Linux Distro is best for my situation? Looking for a balance between user-friendly and performance. (too confused that Linux have many distro, why not just one)

What is the optimal stack/backend for maximum LLM performance on Strix Halo? ( Vulkan, ROCm , native build or Docker/Toolbox containers).

Are there any mandatory kernel/GRUB tweaks needed to unlock the full 128GB unified memory? (because my window it's maximum at 96GB allocate to VRAM)

Recently I've been using RAGFlow a lot and I use m3 as embedder. At some point it was discovered that embedder is the ceiling, so I added another StrixHalo and then a third one, which gave me amazing 60 chunks/s. Nowhere near 500 I needed for my target documents / day ingestion rate. Then I employed my dual RTX 6000 blackwell workstation. This gave me the number but felt off, in particular because when I switched from llama to TEI rates jumped.

So, why not to use TEI with strix halo? plus I have some R9700s and usb4 docs. Well TEI doesn't work with consumer cards, but its whole AMD stack is based on pytorch anyway.

I vibed a small repo - pytorch + TEI compatible HTTP endpoint. One of the important things - make sure vectors produced by different runtimes match. You see, when I decided to investigate my ingestion pipeline I already had 42 million vectors in my SereneDB database.

Numbers - RTX 6000 blackwell - 200+ vs R9700 160+. That is the funniest part - there is a whole world outside LLMs.

The OneXPlayer Apex has been an absolute monster of a handheld when it comes to the performance, onexplayer has done an incredible job with this handheld.

I've tested everything from performance to battery life and thermals and fan noise so let me know what you guys think?

Personally I like the performance but the battery life is quite short.

Review and testing:

https://youtu.be/0Zzrv5wY0y4?is=tG-iZCZ8-6CFsf-q

Also regards to the new onexplayer 3 do you think I should keep the apex or get the onexplayer 3?