Not just LPDDR5, but LPDDR5X-8000 on a 256-bit bus. The 40 CU of RDNA 3.5 is nice, but it's less raw compute than e.g. a desktop 4060 Ti dGPU. The memory is fast, 200+ GB/s real-world read and write (the AIDA64 thread about limited read speeds is misleading, this is what the CPU is able to see, the way the memory controller is configured, but GPU tooling reveals full 200+ GB/s read and write). Though you can only allocate 96 GB to the iGPU on Windows or 110 GB on Linux.
The ROCm and Vulkan stacks are okay, but they're definitely not fully optimized yet.
Strix Halo's biggest weakness compared to Mac setups is memory bandwidth. M4 Max gets something like 500+ GB/s, and M3 Ultra gets something like 800 GB/s, if memory serves correctly.
I just ordered a 128 GB Strix Halo system, and while I'm thrilled about it, but in fariness, for people who don't have an adamant insistence against proprietary kernels, refurbished Apple silicon does offer a compelling alternative with superior performance options. AFAIK there's nothing like Apple Care for any of the Strix Halo systems either.
The 128 GB Strix Halo system was tempting me, but I think I'm going to hold out for the Medusa Point memory bandwidth gains to expand my cluster setup.
I have a Mac Mini M4 Pro 64GB that does quite well with inference on the Qwen3 models, but is hell on networking with my home K3s cluster, which going deeper on is half the fun of this stuff for me.
>The 128 GB Strix Halo system was tempting me, but I think I'm going to hold out for the Medusa Point
I was initially thinking this way too, but I realized a 128GB Strix Halo system would make an excellent addition to my homelab / LAN even once it's no longer the star of the stable for LLM inference - i.e. I will probably get a Medusa Halo system as well once they're available. My other devices are Zen 2 (3600x) / Zen 3 (5950x) / Zen 4 (8840u), an Alder Lake N100 NUC, a Twin Lake N150 NUC, along with a few Pi's and Rockchip SBC's, so a Zen 5 system makes a nice addition to the high end of my lineup anyway. Not to mention, everything else I have maxed out at 2.5GbE. I've been looking for an excuse to upgrade my switch from 2.5GbE to 5 or 10 GbE, and the Strix Halo system I ordered was the BeeLink GTR9 Pro with dual 10GbE. Regardless of whether it's doing LLM, other gen AI inference, some extremely light ML training / light fine tuning, media transcoding, or just being yet another UPS-protected server on my LAN, there's just so much capability offered for this price and TDP point compared to everything else I have.
Apple Silicon would've been a serious competitor for me on the price/performance front, but I'm right up there with RMS in terms of ideological hostility towards proprietary kernels. I'm not totally perfect (privacy and security are a journey, not a destination), but I am at the point where I refuse to use anything running an NT or Darwin kernel.
That is sweet! The extent of my cluster is a few Pis that talk to the Mac Mini over the LAN for inference stuff, that I could definitely use some headroom on. I tried to integrate it into the cluster directly by running k3s in colima - but to join an existing cluster via IP, I had to run colima in host networking mode - so any pods on the mini that were trying to do CoreDNS networking were hitting collisions with mDNSResponder when dialing port 53 for DNS. Finally decided that the macs are nice machines but not a good fit for a member of a cluster.
Love that AMD seems to be closing the gap on the performance _and_ power efficiency of Apple Silicon with the latest Ryzen advancements. Seems like one of these new miniPCs would be a dream setup to run a bunch of data and AI centric hobby projects on - particularly workloads like geospatial imagery processing in addition to the LLM stuff. Its a fun time to be a tinkerer!
It’s not better than the Macs yet. There’s no half assing this AI stuff, AMD is behind even the 4 year old MacBooks.
NVDIA is so greedy that doling out $500 dollars will only you get you 16gb of vram at half the speed of a M1 Max. You can get a lot more speed with more expensive NVDIA GPUs, but you won’t get anything close to a decent amount of vram for less than 700-1500 dollars (well, truly, you will not get close to 32gb even).
Makes me wonder just how much secret effort is being put in by MAG7 to strip NVDIDA of this pricing power because they are absolutely price gouging.