Grace Blackwell needs a new scheduler: 27% more usable GPUs from the same racks
- Not all high-speed interconnects are created equal, and using the wrong one can be quite costly: running the same distributed training job over InfiniBand rather than NVLink can degrade performance by 43%.
- Historically, NVLink was limited to communication within a single node, meaning that multi-node AI workloads were forced to communicate over InfiniBand or a cloud equivalent (EFA on AWS, GPUDirect-TCPX on GCP).
- NVIDIA’s NVL72 systems remove this limitation by connecting all 72 GPUs in a rack with a unified NVLink fabric, so GPUs on different nodes can exchange data with an order of magnitude
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- Not all high-speed interconnects are created equal, and using the wrong one can be quite costly: running the same distributed training job over InfiniBand rather than NVLink can degrade performance by 43%.
- Historically, NVLink was limited to communication within a single node, meaning that multi-node AI workloads were forced to communicate over InfiniBand or a cloud equivalent (EFA on AWS, GPUDirect-TCPX on GCP).
- NVIDIA’s NVL72 systems remove this limitation by connecting all 72 GPUs in a rack with a unified NVLink fabric, so GPUs on different nodes can exchange data with an order of magnitude
Sources: Skypilot