Regarding the DGX Spark and its focus on local AI development, this was a great overview. I'm especially reflecting on the 'large unified memory' aspect. How does this specificly improve the workflow for developing and validating models locally, particularly when considering the eventual transition to superclusters? It seems like a crucial detail for efficiency.
Yes, not having enough VRAM to load models is a big and common problem amongst AI Devs. For local development, most devs rely on building workstations, with either RTX6000 96GB, or have 4 x RTX XX90 cards, to pile up on VRAM memory.
The current problem with piling up multiple GPU's to get more VRAM, is that when you get to finetune or "test" a larger model, you end up with managing memory, setting torchrun for distributed training, sharding models on different GPUs using TensorParallel/PipelineParallel which is a bottleneck sometimes.
Plus, only RTX 50' series have the Blackwell chip features, so if you want to train a model in FP4, or quantize, test and deploy a model on the big Blackwell clusters - your local setup with RTX3090, 4090 - won't do much.
Getting back to memory, Spark's 128gb allows devs to do a lot of things, building and testing locally, and then deploying to the big Blackwell clusters with the lowest friction.
Regarding the DGX Spark and its focus on local AI development, this was a great overview. I'm especially reflecting on the 'large unified memory' aspect. How does this specificly improve the workflow for developing and validating models locally, particularly when considering the eventual transition to superclusters? It seems like a crucial detail for efficiency.
Glad to hear that!
Yes, not having enough VRAM to load models is a big and common problem amongst AI Devs. For local development, most devs rely on building workstations, with either RTX6000 96GB, or have 4 x RTX XX90 cards, to pile up on VRAM memory.
The current problem with piling up multiple GPU's to get more VRAM, is that when you get to finetune or "test" a larger model, you end up with managing memory, setting torchrun for distributed training, sharding models on different GPUs using TensorParallel/PipelineParallel which is a bottleneck sometimes.
Plus, only RTX 50' series have the Blackwell chip features, so if you want to train a model in FP4, or quantize, test and deploy a model on the big Blackwell clusters - your local setup with RTX3090, 4090 - won't do much.
Getting back to memory, Spark's 128gb allows devs to do a lot of things, building and testing locally, and then deploying to the big Blackwell clusters with the lowest friction.
Nice post, learned a lot. In figure 2 it’s cm not mm.
Noted and fixed, Thank you Mauricio!