Comment by coder543
2 days ago
> 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.
2 days ago
> 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.
No, but the models you will be able to run, will run fast and many of them are Good Enough(tm) for quite a lot of tasks already. I mostly use GPT-OSS-120B and glm-4.5-air currently, both easily fit and run incredibly fast, and the runners haven't even yet been fully optimized for Blackwell so time will tell how fast it can go.
You definitely could, the RTX Pro 6000 has 96 (!!!) gigs of memory. You could load 2 experts at once at an MXFP4 quant, or one expert at FP8.
No… that’s not how this works. 96GB sounds impressive on paper, but this model is far, far larger than that.
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.
You don't need a REAP-processed model to offload on a per-expert basis. All MoE models are inherently sparse, so you're only operating on a subset of activated layers when the prompt is being processed. It's more of a PCI bottleneck than a CPU one.
> And I don’t consider running a 1-bit superquant to be a valid thing here either.
I don't either. MXFP4 is scalar.
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