[jsheard]

Any seperation of a GPU from its VRAM is going to come at the expense of (a lot of) bandwidth. VRAM is only as fast as it is because the memory chips are as close as possible to the GPU, either on seperate packages immediately next to the GPU package or integrated onto the same package as the GPU itself in the fanciest stuff.

If you don't care about bandwidth you can already have a GPU access terabytes of memory across the PCIe bus, but it's too slow to be useful for basically anything. Best case you're getting 64GB/sec over PCIe 5.0 x16, when VRAM is reaching 3.3TB/sec on the highest end hardware and even mid-range consumer cards are doing >500GB/sec.

Things are headed the other way if anything, Apple and Intel are integrating RAM onto the CPU package for better performance than is possible with socketed RAM.

[sandworm101]

That depends on whether performance or capacity is the goal. Smaller amounts of ram closer to the processing unit makes for faster computation, but AI also presents a capacity issue. If the workload needs the space, having a boatload of less-fast ram is still preferable to offloading data to something more stable like flash. That is where bulk memory modules connected though slots may one day appear on GPUs.

[duffyjp]

I'm having flashbacks to owning a Matrox Millenium as a kid. I never did get that 4MB vram upgrade.

https://www.512bit.net/matrox/matrox_millenium.html

[mysterydip]

Is there a way to partition the data so that a given GPU had access to all the data it needs but the job itself was parallelized over multiple GPUs?

Thinking on the classic neural network for example, each column of nodes would only need to talk to the next column. You could group several columns per GPU and then each would process its own set of nodes. While an individual job would be slower, you could run multiple tasks in parallel, processing new inputs after each set of nodes is finished.

[zettabomb]

Of course, this is common with LLMs which are too large to fit in any single GPU. I believe Deepspeed implements what you're referring to.