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GPUs and CPUs · AMD Ryzen AI Max

AMD Ryzen AI Max Strix Halo

6owners with an opinion
40%of verdicts positive
2/3positive/negative since 2024
0happy after 3+ years

Praised for performance and value.

What owners wrote

Doesn’t like it
"used to" in your sentence already answers your curiosity. ROCm runs most things just fine without any ceremony or difficulty. I have a couple of the same cards as covered in the article, bought before they got expensive (I'd recommend current Radeon AI Pro 9700 cards over the V620 now, though, as they have increased in price since I bought mine), and they're at the long end of the supported chart for ROCm...nearly EOL. But, they currently work great with llama.cpp and current ROCm 7.14. They're pretty fast and stable. I also have a Strix Halo 128GB, and while the Strix Halo can run bigger models, the dedicated GPUs are quite a bit faster. And, since the best models you can run at home are probably Qwen 3.6 27B and Gemma 4 31B, and those both fit comfortably on dual 32GB GPUs, I find I use the desktop more often than the Strix Halo. Anyway, there's very little reason to spend 3x or more for the Nvidia ecosystem these days. The only exception is the Strix Halo vs the Nvidia GB10 platform. Strix Halo was a no-brainer when it was half the price, but it's risen in price to be almost the same price as an Asus GX10. In that one instance, I think the Nvidia based Asus is a better choice. The Strix Halo is almost too slow to make use of 128GB. Some MoE models are comfortable, like Laguna S2.1, so it's probable that there will someday be an MoE model that is better then Qwen 3.6 27B or Gemma 4 31B that won't run on 64GB but will run on 128GB. I don't know of one, yet, though. AMD al…
Doesn’t like it
i used to mix remote and local minimax 2.7(q3) on my strix halo, it run at 30 tg and 220 tokens pp... it was a bit painful slow, but it was a good feeling i could stay offline. unfortunately m3 which is at opus .8 levels is 460b parameters and doesn't even fit in 128gb of memory, let alone a big context. strix halo feels like a toy for ai purposes. https://kyuz0.github.io/amd-strix-halo-toolboxes/
Neutral
> being able to run a model and being able to run a model fast are two very different thresholds Specifically speaking, on my Strix Halo machine with (theoretical) memory bandwidth of 256 GB/s, a 70 GB model can't generate faster than 256/70= 3.65 t/s. The logic here is that a dense model must do a full read of the weights for each token. So even if the GPU can keep up, the memory bandwidth is limiting. A Mac M5 Pro is faster with a bandwidth of 307 GB/s, but that's only a little faster. This thing is going to be slow on consumer hardware. Maybe that is useful for someone, but I probably prefer a faster model in most cases even if the model isn't quite as smart. Qwen3.6 35B-A3B generates about 50 t/s on my machine, so it can make mistakes, be corrected, and try again in the same time that this model would still be thinking about its first response.
Likes it
I wouldn't call my $2k Strix Halo computer "massively expensive", and it runs e.g. Qwen 3.6 27b brilliantly, with tons of memory to spare and is a full x86 powerhouse pulling 120w at absolute max. IMO the programming world is far too myopic about / insistent on using laptops, especially macbooks. Just because a crappy deal exists doesn't mean everyone is forced to take it. Local AI is a high performance computing problem and laptops are fundamentally a crappy form factor for it; buy an efficient desktop computer and be surprised at what's possible even with today's crazy prices.
Likes it
I think that speaks to them not understanding at the time the opportunity they were missing out on by not shipping a CUDA-like thing to everyone, including consumer tech. The question is what'll it look like in a few years now that they do understand AI is the biggest part of the GPU industry. I suspect, given AMDs relative openness vs. nvidia, even consumer-level stuff released today will end up with a longer useful life than current nvidia stuff. I could be wrong, of course. I've taken the gamble...the last nvidia GPU I bought was a 3070 several years ago. Everything recent has been AMD. It's half the price for nearly competitive performance and VRAM. If that bet turns out wrong, I'll just upgrade a little sooner and still probably end up ahead. But, I think/hope openness will win. Also, nvidia graphics drivers on Linux are a pain in the ass that I didn't want to keep dealing with. I decided it wasn't worth the hassle, even if they're better on some metrics. I've been able to run everything I've tried on an AMD Strix Halo and an old Radeon Pro V620 (not great, but cheap, compared to other 32GB GPUs and still supported by current ROCm).
Likes itHasn’t owned it
I think the only thing its lacking compared to the base PS5 is memory bandwidth (256GB/s vs 448GB/s) but in exchange you get much lower latency. Everything else is a massive upgrade over the PS5. The cores are way newer, clocked higher, and you can get twice as many. The CUs are newer, clocked higher, and more numerous as well. It can be specced up to 8x the ram of the PS5. Is there a workload where that 75% more bandwidth is more important than raw perf or access to more than 16GB of ram?