The point in this thread is that it would likely be too slow due to prompt processing. (M5 Ultra might fix this with the GPU's new neural accelerators.)
Please do give that a try and report back the prefill and decode speed. Unfortunately, I think again that what I wrote earlier will apply:
> In practice, it'll be incredible slow and you'll quickly regret spending that much money on it
I'd rather place that 10K on a RTX Pro 6000 if I was choosing between them.
One RTX Pro 6000 is not going to be able to run GLM-4.7, so it's not really a choice if that is the goal.
If you are running a REAP model (eliminating experts), then you are not running GLM-4.7 at that point — you’re running some other model which has poorly defined characteristics. If you are running GLM-4.7, you have to have all of the experts accessible. You don’t get to pick and choose.
If you have enough system RAM, you can offload some layers (not experts) to the GPU and keep the rest in system RAM, but the performance is asymptotically close to CPU-only. If you offload more than a handful of layers, then the GPU is mostly sitting around waiting for work. At which point, are you really running it “on” the RTX Pro 6000?
If you want to use RTX Pro 6000s to run GLM-4.7, then you really need 3 or 4 of them, which is a lot more than $10k.
And I don’t consider running a 1-bit superquant to be a valid thing here either. Much better off running a smaller model at that point. Quantization is often better than a smaller model, but only up to a point which that is beyond.
> And I don’t consider running a 1-bit superquant to be a valid thing here either.
I don't either. MXFP4 is scalar.
You're better off prioritizing the offload of the KV cache and attention layers to the GPU than trying to offload a specific expert or two, but the performance loss I was talking about earlier still means you're not offloading enough for a 96GB GPU to make things how they need to be. You need multiple, or you need a Mac Studio.
If someone buys one of these $8000 GPUs to run GLM-4.7, they're going to be immensely disappointed. This is my point.
Absolutely, same if they get a $10K Mac/Apple computer, immense disappointment ahead.
Best is of course to start looking at models that fit within 96GB, but that'd make too much sense.
This almost feels like a retro computing kind of hobby than anything aimed at genuine productivity.
Maybe I'm old school, but I prefer those benefits over some cost/benefit analysis across 4 years which by the time we're 20% through it, everything has changed.
But I also use this hardware for training my own models, not just inference and not just LLMs, I'd agree with you if we were talking about just LLM inference.
M4 Max here w/ 128GB RAM. Can confirm this is the bottleneck.
I weighed about a DGX Spark but thought the M4 would be competitive with equal RAM. Not so much.
However it will be better for training / fine tuning, etc. type workflows.
For the DGX benchmarks I found, the Spark was mostly beating the M4. It wasn't cut and dry.
The M4 Max has double the memory bandwidth, so it should be faster for decode (token generation).
Because Apple has not adjusted their pricing yet for the new ram pricing reality. The moment they do, its not going to be a $10k system anymore but in the $15k+...
The amount of wafers going to AI is insane and will influence not just memory prices. Do not forget, the only reason why Apple is currently immunity to this, is because they tend to make long term contracts but the moment those expire ... then will push the costs down consumers.