NVIDIA GB300 NVL72

NVIDIA's Blackwell Ultra rack with 72 GPUs and 36 Grace CPUs in one NVLink domain, 20 TB of HBM3e, 130 TB/s NVLink and 1,440 PFLOPS sparse FP4.

Family
Blackwell Ultra
Edition
Rack scale system, 72 GPUs and 36 Grace CPUs
Memory
20 TB HBM3e GPU memory per rack (288 GB per GPU), 17 TB LPDDR5X CPU memory, 37 TB fast memory
Memory bandwidth
Up to 576 TB/s GPU memory bandwidth per rack (8 TB/s per GPU), 14 TB/s CPU memory bandwidth
Interconnect
Fifth generation NVLink across all 72 GPUs, 130 TB/s total NVLink bandwidth, NVLink-C2C to Grace
Form factor
Single liquid cooled rack with 18 compute trays and 9 NVLink switch trays
TDP
Up to 1,400 W per GPU, as reported in NVIDIA's Blackwell Ultra technical material; rack level power not yet published on the GB300 NVL72 product page
Cooling
Liquid
Best for
Reasoning model inference and training at rack scale, where 72 GPUs act as one NVLink domain
Availability
Allocation required

GB300 NVL72 connects 72 Blackwell Ultra GPUs and 36 NVIDIA Grace CPUs in a single liquid cooled rack as one 72 GPU NVLink domain. NVIDIA publishes 20 TB of GPU memory at up to 576 TB/s, 17 TB of LPDDR5X CPU memory, 37 TB of fast memory, 130 TB/s of NVLink bandwidth, 2,592 Arm Neoverse V2 cores, and 1,440 PFLOPS of sparse FP4 (1,080 PFLOPS dense) per rack.

The rack is designed for test time scaling inference and for training runs that benefit from a large NVLink domain. It is delivered as a complete system with compute trays, NVLink switch trays, power shelves and the coolant distribution interface, and it requires a liquid cooled data center with the matching facility water loop. We quote GB300 NVL72 as a project with site requirements and a Quantum-X800 or Spectrum-X scale out fabric, delivered in phases and commissioned in your liquid cooled hall.

Systems that carry this GPU

Related guides

Air cooled vs liquid cooled AI servers

How to decide between air and direct liquid cooling for GPU servers, from the rack power limit up to NVL72 racks that only come liquid cooled.

5 min read

In this guide

  1. The question is the rack, not the server
  2. How air cooling works at high density

Want this GPU in a different chassis?

We quote every class that carries it, from a single node to a pod. Tell us the workload and we propose the chassis.