1.5 TB of VRAM on Mac Studio – RDMA over Thunderbolt 5
11–20 of 236 posts
Re: 1.5 TB of VRAM on Mac Studio – RDMA over Thunderbolt 5
#12Re: 1.5 TB of VRAM on Mac Studio – RDMA over Thunderbolt 5
#13My expectations from M5 Max/Ultra devices: - Something like DGX QSFP link (200Gb/s, 400Gb/s) instead of TB5. Otherwise, the economies of this RDMA setup, while impressive, don't make sense. - Neural accelerators to get prompt prefill time down. I don't expect RTX 6000 Pro speeds, but something like 3090/4090 would be nice. - 1TB of unified memory in the maxed out version of Mac Studio. I'd rather invest in more RAM t…
This isn’t any different with QSFP unless you’re suggesting that one adds a 200GbE switch to the mix, which:
* Adds thousands of dollars of cost,
* Adds 150W or more of power usage and the accompanying loud fan noise that comes with that,
* And perhaps most importantly adds measurable latency to a networking stack that is already higher latency than the RDMA approach used by the TB5 setup in the OP.
Re: 1.5 TB of VRAM on Mac Studio – RDMA over Thunderbolt 5
#14My expectations from M5 Max/Ultra devices: - Something like DGX QSFP link (200Gb/s, 400Gb/s) instead of TB5. Otherwise, the economies of this RDMA setup, while impressive, don't make sense. - Neural accelerators to get prompt prefill time down. I don't expect RTX 6000 Pro speeds, but something like 3090/4090 would be nice. - 1TB of unified memory in the maxed out version of Mac Studio. I'd rather invest in more RAM t…
Apple Neural Engine is a thing already, with support for multiply-accumulate on INT8 and FP16. AI inference frameworks need to add support for it.
> this setup can support up to 4 Mac devices because each Mac must be connected to every other Mac!!
Do you really need a fully connected mesh? Doesn't Thunderbolt just show up as a network connection that RDMA is ran on top of?
Re: 1.5 TB of VRAM on Mac Studio – RDMA over Thunderbolt 5
#15Re: 1.5 TB of VRAM on Mac Studio – RDMA over Thunderbolt 5
#16Re: 1.5 TB of VRAM on Mac Studio – RDMA over Thunderbolt 5
#17My expectations from M5 Max/Ultra devices: - Something like DGX QSFP link (200Gb/s, 400Gb/s) instead of TB5. Otherwise, the economies of this RDMA setup, while impressive, don't make sense. - Neural accelerators to get prompt prefill time down. I don't expect RTX 6000 Pro speeds, but something like 3090/4090 would be nice. - 1TB of unified memory in the maxed out version of Mac Studio. I'd rather invest in more RAM t…
M4 already hit the necessary speed per channel, and M5 is well above it. If they actually release an Ultra that much bandwidth is guaranteed on the full version. Even the smaller version with 25% fewer memory channels will be pretty close.
We already know Max won't get anywhere near 1TB/s since Max is half of an Ultra.
Re: 1.5 TB of VRAM on Mac Studio – RDMA over Thunderbolt 5
#18Makes one wonder what apple uses for their own servers. I guess maybe they have some internal M-series server product they just haven’t bothered to release to the public, and features like this are downstream of that?
Re: 1.5 TB of VRAM on Mac Studio – RDMA over Thunderbolt 5
#19My expectations from M5 Max/Ultra devices: - Something like DGX QSFP link (200Gb/s, 400Gb/s) instead of TB5. Otherwise, the economies of this RDMA setup, while impressive, don't make sense. - Neural accelerators to get prompt prefill time down. I don't expect RTX 6000 Pro speeds, but something like 3090/4090 would be nice. - 1TB of unified memory in the maxed out version of Mac Studio. I'd rather invest in more RAM t…
> Neural accelerators to get prompt prefill time down. Apple Neural Engine is a thing already, with support for multiply-accumulate on INT8 and FP16. AI inference frameworks need to add support for it. > this setup can support up to 4 Mac devices because each Mac must be connected to every other Mac!! Do you really need a fully connected mesh? Doesn't Thunderbolt just show up as a network connection that RDMA is ran…
Re: 1.5 TB of VRAM on Mac Studio – RDMA over Thunderbolt 5
#20The largest nodes in his cluster each have 512GB RAM. DeepSeek V3.1 is a 671B parameter model whose weights take up 700GB RAM: https://huggingface.co/deepseek-ai/DeepSeek-V3.1 I would have expected that going from one node (which can't hold the weights in RAM) to two nodes would have increased inference speed by more than the measured 32% (21.1t/s -> 27.8t/s). With no constraint on RAM (4 nodes) the inference speed i…