This is more of an ad, not a review, and reads like the author has hardly any experience with the things he's trying out. That Z Image Turbo diffusion model would've also run on many consumer GPUs and with way higher performance for a fraction of the price. Misleading.
Highly recommend lemonade server if you have a strix halo desktop. Been using Qwen3.6-35B @ Q_8 as my main driver and it’s been great with 60 TPS for generation. I occasionally use the 27B @ q6 but only get 20-25 TPS for generation with MTP.
Does anyone else feel like it would be great to be able to purchase $4000 AI box but 128 gigs is not enough. If I spend all that money and it doesn’t really do what I wanted to do, whats the point?
It’s kind of like general aviation where you can go buy a Cessna but it’s only going to realistically get you somewhere you could drive anyways but do you really wanna spend that mush cash to get road trip distance at slightly better than road trip speeds? You really need a 5 million dollar jet and that’s just not practical. That’s sort of how I feel about this device.
The Strix Halo is a great dev machine and a mediocre AI machine. You can run Qwen 3.6 27B at a decent speed, or larger MoE models, and that's about it. For some that's more than enough though, myself included.
Unfortunately, the table of models and tokens per second (TPS) and time to first token (TTFT) is not helpful without specifying the quantization of the model.
Until RAM prices drop and can economically get machines with 256GB, 512GB and higher bandwidth... I frankly think the local AI story is going to be still fairly muted for most people.
My Spark can do Qwen3.6 MoE A3B at 60 to 70-ish token/second and that's really good, but there's limits the usefulness of that model. It's not useful for coding, in any case.
Once people can run something like GLM 5.2 at lower quants (512GB could do a passable job), then I think the story changes.
Whether we ever see DRAM as cheap as it was ever again, I don't know.
Agree - the 128GB Strix Halo is capable if you use LLMs as assistants, but it's not so good if you use LLMs as agents (or worse, agent teams/swarms) since all of the models that can fit on it are pretty dumb compared to frontier or near-frontier models. You can at best hope for Sonnet-level capabilities.
That doesn't mean that local models are useless though! If Mythos/Sol is an ASI that threatens to take your job and turn you into paperclips, then Qwen/Gemma is an old-fashioned office secretary that loyally helps you with tasks but doesn't have a good grasp of details. Every white-collar worker 50 years ago would have killed to have a hard-working personal secretary.
Part of me wonders, would 3d xpoint (if still around) be a viable option?
Yeah it is slower than real RAM by a good amount for latency, but you can get similar bandwidth and the cost was history about half of the same size DDR.
Possibly - I've heard anecdotal reports that old Intel Optane chips are in hot demand right now. Intel/Micron probably would have made a killing if they had kept that product line alive for a few more years. Never miss an attempt to snatch defeat from the jaws of victory!
I was thought experimenting the other day... ~10 nVME drives striped and running parallel could approximate the memory bandwidth of DDR5 DRAM in a box like this. Like you say, latency wouldn't compete but on raw throughput would be comparable.
Not anymore cost effective, I guess, but gets you the ability to work over very large model sizes maybe. But the problem is that tensor matmul etc hardware wouldn't work effectively with it.
It’s kind of like general aviation where you can go buy a Cessna but it’s only going to realistically get you somewhere you could drive anyways but do you really wanna spend that mush cash to get road trip distance at slightly better than road trip speeds? You really need a 5 million dollar jet and that’s just not practical. That’s sort of how I feel about this device.
My Spark can do Qwen3.6 MoE A3B at 60 to 70-ish token/second and that's really good, but there's limits the usefulness of that model. It's not useful for coding, in any case.
Once people can run something like GLM 5.2 at lower quants (512GB could do a passable job), then I think the story changes.
Whether we ever see DRAM as cheap as it was ever again, I don't know.
That doesn't mean that local models are useless though! If Mythos/Sol is an ASI that threatens to take your job and turn you into paperclips, then Qwen/Gemma is an old-fashioned office secretary that loyally helps you with tasks but doesn't have a good grasp of details. Every white-collar worker 50 years ago would have killed to have a hard-working personal secretary.
Yeah it is slower than real RAM by a good amount for latency, but you can get similar bandwidth and the cost was history about half of the same size DDR.
Not anymore cost effective, I guess, but gets you the ability to work over very large model sizes maybe. But the problem is that tensor matmul etc hardware wouldn't work effectively with it.
Useful for KVCache though.