NVIDIA GB300
NVL72
The NVIDIA GB300 NVL72 is a liquid-cooled, rack-scale system that unifies 72 NVIDIA Blackwell Ultra GPUs and 36 Arm-based NVIDIA Grace CPUs into a single platform built for test-time scaling and AI reasoning.Â

Rack-scale performance for AI reasoning
MORE AI FACTORY
OUTPUT*
MORE AI FACTORY
OUTPUT*
Up to a 50x increase in AI factory output for reasoning-model inference compared to an NVIDIA H100 Tensor Core GPU, combining higher throughput and lower latency for maximum token revenue.
BETTER LATENCY
(TPS / USER)*
BETTER LATENCY
(TPS / USER)*
Up to 10x boost in user responsiveness (tokens per second per user) compared to an NVIDIA H100 Tensor Core GPU, enabling faster, more responsive interactive AI experiences.
COHERENT FAST
MEMORY PER RACK
COHERENT FAST
MEMORY PER RACK
Up to 40TB of coherent fast memory across 72 Blackwell Ultra GPUs and 36 Grace CPUs, including up to 21TB of HBM3E, so trillion-parameter models and long-context reasoning stay resident in a single NVLink domain.
FASTER REAL-TIME
VIDEO GENERATION*
FASTER REAL-TIME
VIDEO GENERATION*
Up to 30x faster real-time video generation than Hopper for world foundation models and physical AI such as robotics and autonomous vehicles.
Build the future faster with Crusoe Cloud
Up to 20 times faster and 81% less expensive than traditional cloud providers.
Frequently
asked questions
What is the NVIDIA GB300 NVL72?
The NVIDIA GB300 NVL72 is a rack-scale system that connects 72 Blackwell Ultra GPUs and 36 Arm-based NVIDIA Grace CPUs over fifth-generation NVLink into a single, unified accelerator. It is designed for large-scale reasoning models, agentic AI, and trillion-parameter inference, and reuses the NVL72 rack design for a smooth upgrade from GB200.
What is the difference between NVIDIA GB300 NVL72 and GB200 NVL72?
Both are Grace Blackwell NVL72 rack-scale systems, and GB300 NVL72 is the Blackwell Ultra generation with more GPU memory and higher inference performance tuned for AI reasoning and test-time scaling. GB300 NVL72 keeps the same NVL72 rack architecture as GB200 NVL72, so it is a performance step up rather than a redesign.
Is the NVIDIA GB300 NVL72 better for training or inference?
It handles both, but it is optimized for large-scale inference, where it delivers up to 50x more AI factory output than Hopper. Its large HBM3E memory and high attention performance also make it strong for training and post-training trillion-parameter models.
What is the difference between the NVIDIA GB300 and B300?
The GB300 is the Grace Blackwell rack-scale system, 72 Blackwell Ultra GPUs plus 36 Grace CPUs in one NVL72 rack. The HGX B300 is an 8 Blackwell Ultra GPU system. Choose GB300 for the largest rack-scale reasoning and training workloads, and B300 for powerful AI and HPC at flexible single-system scale.


