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GB200 NVL72 — Performance at a Glance

30x

Faster LLM Inference vs. H100

4x

Faster LLM Training vs. H100

25x

More Efficient at Same Power

1.44 ExaFLOPS

FP4 Compute Per Rack

GPU rig

What is the NVIDIA GB200 NVL72?

The NVIDIA GB200 NVL72 is a liquid-cooled, rack-scale AI supercomputer that combines 72 NVIDIA Blackwell GPUs and 36 Grace CPUs into a single unified system. Available through GPU as a Service (GPUaaS) from Cyfuture AI, it is powered by a 130 TB/s NVLink fabric, enabling all GPUs to function as one massive compute engine and delivering exceptional performance for 200B+ parameter model training and 671B-scale LLM inference.

At its core are 36 Grace Blackwell Superchips, each featuring two B200 GPUs and one Grace CPU connected via 900 GB/s NVLink-C2C, eliminating traditional CPU-GPU bottlenecks and enabling faster, more efficient AI workloads. Through Cyfuture AI's GPU as a Service, enterprises can access this exascale AI infrastructure on demand without the complexity of deploying and managing rack-scale hardware.

Deploy the NVIDIA GB200 NVL72
and Build AI Without Limits

Access the world's most powerful rack-scale AI supercomputer with 72 Blackwell GPUs, 13.4TB unified memory, and 30x faster LLM inference.
Scale from a single Superchip to a full NVL72 rack with enterprise-grade infrastructure from Cyfuture AI.

Get GB200 NVL72 Pricing

Technical Specifications
NVIDIA GB200 NVL72 Technical Specifications

Specification Value
Architecture NVIDIA Blackwell (5th Generation)
System Configuration 36 Grace Blackwell Superchips | 72 Blackwell B200 GPUs | 36 Grace ARM CPUs
Total GPU Memory 13.4 TB HBM3e
GPU Memory Bandwidth 576 TB/s (aggregate across all 72 GPUs)
NVLink Interconnect (All-to-All) 130 TB/s (5th Gen NVLink Switch System)
NVLink-C2C Bandwidth (CPU–GPU) 900 GB/s per Superchip
NVFP4 Performance (with sparsity) 1,440 PFLOPS (per rack) | 40 PFLOPS (per Superchip)
NVFP4 Performance (dense) 720 PFLOPS (per rack) | 20 PFLOPS (per Superchip)
FP8 / FP6 Tensor Core Performance 720 PFLOPS (per rack) | 20 PFLOPS (per Superchip)
INT8 Tensor Core Performance 720 POPS (per rack) | 20 POPS (per Superchip)
FP16 / BF16 Tensor Core Performance 360 PFLOPS (per rack) | 10 PFLOPS (per Superchip)
TF32 Tensor Core Performance 180 PFLOPS (per rack) | 5 PFLOPS (per Superchip)
FP32 Performance 5,760 TFLOPS (per rack) | 160 TFLOPS (per Superchip)
FP64 / FP64 Tensor Core 2,880 TFLOPS (per rack) | 80 TFLOPS (per Superchip)
CPU Core Count (Grace) 2,592 Arm Neoverse V2 cores (per rack) | 72 cores (per Superchip)
CPU Memory (LPDDR5X) 17 TB (per rack) | Up to 480 GB (per Superchip)
CPU Memory Bandwidth 14 TB/s (per rack) | Up to 512 GB/s (per Superchip)
Transformer Engine 2nd Generation — FP4 / FP8 / FP6 microscaling formats
Cooling Direct Liquid Cooling (DLC) — required; air cooling not viable at 120 kW rack density
Rack Power Draw ~120 kW (120–132 kW under full load)
Rack Form Factor 48U OCP Open Rack V3 (600mm wide × 1,068mm deep)
Rack Weight ~1.36 metric tons
Networking (Scale-Out) NVIDIA Quantum-X800 InfiniBand (NDR 400 Gb/s / XDR 800 Gb/s) or Spectrum-X800 Ethernet
Network Adapters ConnectX-7 (NDR, earlier deployments) | ConnectX-8 (XDR 800 Gb/s, mid-2025 onward)
Supported Precision Formats FP4, FP6, FP8, BF16, FP16, TF32, FP32, FP64, INT8

Why Access the GB200 NVL72 Through Cyfuture AI?

01

Enterprise-Grade AI Infrastructure, Fully Managed

Cyfuture AI operates enterprise-class AI data center infrastructure engineered for the power, cooling, and floor-load demands of the GB200 NVL72. A single GB200 NVL72 rack draws 120–132 kW, weighs 1.36 metric tons, and requires dedicated 3-phase power circuits, DLC cooling manifolds, and reinforced flooring. Our facilities are purpose-built to handle this at scale — so your team focuses on training runs and inference pipelines, not infrastructure operations.

02

Rack-Scale Access Without Hyperscaler Lock-In

Major cloud providers offering GB200 NVL72 access charge $10.50–$27 per GPU-equivalent per hour — translating to $756–$1,944 per hour for a full 72-GPU rack. Cyfuture AI delivers competitive, transparent access to the same NVIDIA Blackwell AI infrastructure through flexible commitment structures designed for enterprise AI teams, research institutions, and sovereign AI deployments — without proprietary lock-in to a single hyperscaler's ecosystem.

03

Proven AI Infrastructure Expertise

With years of experience deploying GPU clusters, AI data centers, and GPU-as-a-Service infrastructure, Cyfuture AI understands what enterprise AI teams actually need: consistent uptime, rapid provisioning, expert technical support, and infrastructure that scales with model and team growth. We have supported AI training and inference workloads for enterprises across BFSI, healthcare, manufacturing, and public sector.

04

White-Glove Deployment and Ongoing Support

From initial infrastructure consultation through deployment, ongoing monitoring, and hardware-level incident response, Cyfuture AI provides 24×7 expert technical support. Our team handles the complexity of rack-scale AI deployment — NVLink configuration, InfiniBand fabric setup, DLC system management, and software stack integration — so your engineers can stay focused on building models.

05

Flexible Configuration — Single Superchip to Full Rack

Not every workload requires all 72 GPUs. Cyfuture AI enables flexible access to GB200 NVL72 capacity — from a single Grace Blackwell Superchip (2 B200 GPUs + 1 Grace CPU) up to full rack-scale 72-GPU configurations — matched to your actual workload requirements and growth trajectory. Scale up as your training runs and inference demands grow.

06

India-Headquartered, Data-Sovereign AI Infrastructure

For enterprises with data residency requirements, regulated AI workloads, or sovereign AI mandates, Cyfuture AI's India-based infrastructure provides a compliant, secure alternative to offshore hyperscaler deployments. Your data, your models, and your compute stay within the jurisdiction you require.

AI Server Illustration

Architectural Breakthroughs That Define the GB200 NVL72

1. Blackwell Architecture — The 6th GPU Generation

NVIDIA's Blackwell architecture introduces second-generation Transformer Engine with FP4 microscaling precision — doubling throughput compared to FP8 on Hopper with minimal accuracy loss. New microscaling formats (MX-FP4, MX-FP6, MX-FP8) optimize tensor operations for both high-throughput training and low-latency inference. The 5th-generation NVLink Switch System with 130 TB/s all-to-all bandwidth is a Blackwell-exclusive innovation that makes the entire NVL72 rack behave as a single, coherent GPU.

2. 13.4 TB Unified GPU Memory — Run Any Model in One Rack

The GB200 NVL72's 13.4 TB of HBM3e GPU memory, shared across a unified NVLink domain, enables enterprises to load and serve models that would require multiple interconnected server nodes on any prior generation. DeepSeek R1 671B in FP8 needs roughly 700–750 GB for weights and runtime buffers — the NVL72 holds it entirely in one rack, with room for large KV caches at high concurrency. Mixture-of-Experts (MoE) models with 1.8 trillion parameters can be trained at scale using the full rack as a single compute unit.

3. 130 TB/s NVLink Fabric — Eliminate the InfiniBand Bottleneck

Traditional multi-node GPU clusters synchronize gradients and KV cache across InfiniBand at 400 Gb/s (50 GB/s). For a 200B parameter model across nine 8×B200 nodes, gradient synchronization adds 400–600 ms per training step. The NVL72's 130 TB/s all-to-all NVLink cuts that overhead to microseconds, shifting the bottleneck back to pure compute. This is the single most important reason to choose the GB200 NVL72 for 200B+ parameter model training.

4. NVLink-C2C: CPU–GPU Memory Coherence at 900 GB/s

Each Grace CPU connects to its two Blackwell GPUs via NVLink-C2C — a coherent, cache-coherent interconnect at 900 GB/s, versus PCIe Gen 5's 128 GB/s. This allows the Grace CPU and B200 GPUs to share memory directly without staging data copies, which is especially impactful for multi-modal pipelines (vision-language models, video generation) where preprocessing runs on the CPU and feeds directly into GPU attention layers. The PCIe bottleneck is eliminated entirely.

5. Liquid Cooling at Rack Scale — 25x More Efficient than H100 Air-Cooled

At 120 kW per rack, direct liquid cooling (DLC) is not optional — it is architecturally required. The benefit is substantial: the GB200 NVL72 delivers 25x more AI performance at the same power envelope compared to H100 air-cooled infrastructure. Liquid cooling also increases compute density, reduces data center floor space requirements, and enables the high-bandwidth NVLink domain architecture that would be thermally impossible with air cooling.

Real-World Applications of the NVIDIA GB200 NVL72

Frontier AI Training

Frontier AI Training

Train 200B to trillion-parameter models with up to 4x faster throughput than H100 clusters.

Production LLM Inference

Production LLM Inference

Run 671B-scale models like DeepSeek R1 and Llama 3.1 with 30x faster inference and lower latency.

MoE Model Acceleration

MoE Model Acceleration

Deliver up to 10x better performance for Mixture-of-Experts (MoE) architectures with ultra-fast expert routing.

Multi-Modal AI

Multi-Modal AI

Power vision-language, video generation, and audio AI workloads with 30-50% lower latency.

Scientific Computing & HPC

Scientific Computing & HPC

Accelerate molecular dynamics, climate modeling, genomics, and other compute-intensive simulations.

Enterprise Private AI

Enterprise Private AI

Deploy secure, dedicated AI infrastructure for regulated industries requiring data sovereignty and compliance.

Generative AI at Hyperscale

Generative AI at Hyperscale

Support high-concurrency text, code, image, video, and agentic AI applications with 1.44 ExaFLOPS of compute in a single rack.

Ready to Power Your
Next Generation of AI?

Train trillion-parameter models, deploy frontier LLMs, and accelerate generative
AI workloads on the NVIDIA GB200 NVL72 with fully managed infrastructure and expert support.

Buy NVIDIA GB200 NVL72
H200 GPUs

Voices of Innovation: How We're Shaping AI Together

We're not just delivering AI infrastructure-we're your trusted AI solutions provider, empowering enterprises to lead the AI revolution and build the future with breakthrough generative AI models.

KPMG optimized workflows, automating tasks and boosting efficiency across teams.

H&R Block unlocked organizational knowledge, empowering faster, more accurate client responses.

TomTom AI has introduced an AI assistant for in-car digital cockpits while simplifying its mapmaking with AI.

GB200 NVL72 vs. H100 vs. B200 — Which Do You Need?

Not every workload needs rack-scale infrastructure. Here is a clear-eyed comparison to help you make the right choice:

Specification 8×H100 SXM5 8×B200 HGX GB200 NVL72 (Full Rack)
Total GPU Memory 640 GB (8×80 GB) 1.44 TB (8×180 GB) 13.4 TB (72 B200 GPUs)
NVLink Bandwidth 900 GB/s per GPU (intra-node) 1.8 TB/s per GPU (intra-node) 130 TB/s all-to-all (all 72 GPUs)
FP4 Compute (sparsity) N/A 144 PFLOPS 1,440 PFLOPS
Max Model Size (FP16) ~300B (1 node) ~650B (1 node) ~671B (1 rack, full model in memory)
Cross-Node Bandwidth InfiniBand 400 Gb/s (50 GB/s) InfiniBand 400 Gb/s (50 GB/s) NVLink 130 TB/s (no InfiniBand hops)
LLM Inference vs. H100 1x (baseline) ~5–8x 30x
Best For Sub-70B models, mature stack 70B–100B models, FP4 workloads 200B+ training, 671B inference, MoE
Power Per Unit ~5.6 kW per node ~8 kW per node ~120 kW per rack

The GB200 NVL72 wins decisively when: (1) your model exceeds 100B parameters and all-reduce bandwidth is the bottleneck; (2) you need to serve a 671B-scale model entirely in one rack's memory for consistent latency; or (3) your MoE routing patterns require the all-to-all NVLink fabric to avoid InfiniBand expert dispatch overhead. For smaller models, a cluster of 8×H100 or 8×B200 nodes remains more cost-effective.

Infrastructure Requirements — What the GB200 NVL72 Demands

The GB200 NVL72 is not standard colo hardware. It requires dedicated data center infrastructure capable of meeting these requirements:

Power

Power

~120–132 kW per rack under full load. Requires dedicated 3-phase power circuits rated for the full load, typically a dedicated PDU with 200A+ capacity. Standard 20 kW-per-rack data center allocations are insufficient by a factor of 6x.

Cooling

Cooling

Direct Liquid Cooling (DLC) is required — air cooling is not viable at 120 kW rack density. Rear-door heat exchangers designed for 30–40 kW racks cannot handle this load. DLC manifolds must be installed for the GPU and CPU components.

Floor Load Capacity

Floor Load Capacity

Approximately 1.36 metric tons per rack. Standard raised-floor tiles rated for 250 kg per tile require reinforced flooring or a dedicated slab with verified load capacity before installation.

Rack Form Factor

Rack Form Factor

48U OCP Open Rack V3 format (600mm wide × 1,068mm deep). This is not a standard 19-inch data center rack. Additional space is needed for cabling and coolant manifold connections.

Networking (Scale-Out)

Networking (Scale-Out)

NVIDIA Quantum-X800 InfiniBand (NDR 400 Gb/s or XDR 800 Gb/s) or Spectrum-X800 Ethernet for cross-rack GPU communication when scaling beyond a single NVL72 rack. ConnectX-8 SuperNICs for XDR 800 Gb/s on systems deployed from mid-2025 onward.

Cyfuture AI's data center infrastructure is purpose-built to meet all of these requirements at scale. Our liquid-cooled AI data centers, including our 30 MW Chennai facility, are engineered for the next generation of rack-scale NVIDIA Blackwell AI infrastructure.

Software Stack — Everything the GB200 NVL72 Supports

NVIDIA AI Enterprise Suite
NVIDIA AI Enterprise Suite

Production-grade AI software for enterprise deployment

CUDA 12.x
CUDA 12.x

Full CUDA support with Blackwell-optimized kernels

CUDA-X Libraries
CUDA-X Libraries

cuDNN, cuBLAS, NCCL, RAPIDS, cuSPARSE, and more

NVIDIA Triton Inference Server
NVIDIA Triton Inference Server

Optimized multi-model, multi-framework inference serving

TensorRT-LLM
TensorRT-LLM

NVIDIA's high-performance LLM inference engine with FP4/FP8 support

PyTorch 2.x
PyTorch 2.x

Full GB200 NVLink support with distributed training across NVL72 domain

TensorFlow 2.x
TensorFlow 2.x

GPU-accelerated training and inference

JAX
JAX

XLA-compiled high-performance training with Blackwell backends

NVIDIA NeMo
NVIDIA NeMo

Large-scale LLM and multi-modal training framework

NVIDIA Magnum IO
NVIDIA Magnum IO

Software stack for high-throughput distributed training and I/O

vLLM
vLLM

High-throughput LLM serving with PagedAttention, compatible with Blackwell

DeepSpeed / Megatron-LM
DeepSpeed / Megatron-LM

Distributed training frameworks for trillion-parameter models

NVIDIA Base Command Manager
NVIDIA Base Command Manager

Centralized workload and cluster management

NVIDIA Mission Control
NVIDIA Mission Control

AI factory operations management for GB200 NVL72 deployments

Access the NVIDIA GB200 NVL72 — The Cyfuture AI Advantage

Cyfuture AI delivers access to the NVIDIA GB200 NVL72 GPU Server with the infrastructure depth, operational expertise, and enterprise support that this class of hardware demands. We are not just a reseller — we are an AI infrastructure operator with the data center capacity, engineering capability, and enterprise relationships to deploy and manage rack-scale NVIDIA Blackwell AI servers reliably.

Purpose-Built Data Center
Infrastructure

Liquid-cooled, high-density facilities engineered for 120 kW+ rack deployments

Flexible Access
Models

From dedicated Superchip access to full 72-GPU rack configurations

Transparent, Competitive
Pricing

Enterprise AI GPU Server access without hyperscaler lock-in

24×7 Expert
Support

Round-the-clock access to AI infrastructure engineers

India-Sovereign
Compute

For enterprises with data residency and compliance requirements

Turnkey
Deployment

Pre-configured NVLink, networking, storage, and software stack integration

Trusted by Industry
Leaders

NVIDIA partner, serving enterprises across banking, healthcare, manufacturing, and public sector

Trusted by Industry leaders

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FAQs: NVIDIA GB200

The power of AI, backed by human support

At Cyfuture AI, we combine advanced technology with genuine care. Our expert team is always ready to guide you through setup, resolve your queries, and ensure your experience with Cyfuture AI remains seamless. Reach out through our live chat or drop us an email at [email protected] - help is only a click away.

The NVIDIA GB200 NVL72 is a rack-scale AI supercomputer integrating 72 NVIDIA Blackwell B200 GPUs and 36 Grace ARM CPUs across 36 Grace Blackwell Superchips in a single liquid-cooled rack. Connected by a 130 TB/s NVLink Switch fabric, all 72 GPUs act as one unified compute domain, delivering 1.44 ExaFLOPS of FP4 compute, 13.4 TB of GPU memory, and 30x faster LLM inference than the NVIDIA H100.

The GB200 NVL72 provides 13.4 TB of HBM3e GPU memory across all 72 Blackwell GPUs, with 576 TB/s aggregate memory bandwidth. Each Grace Blackwell Superchip carries 372 GB of GPU HBM3e and up to 480 GB of CPU LPDDR5X memory, accessible at 512 GB/s.

The 13.4 TB unified GPU memory enables the GB200 NVL72 to hold and serve models that cannot fit in any single multi-GPU node, including DeepSeek R1 671B, GPT-4-class models, and trillion-parameter MoE architectures. Models up to approximately 671B parameters can be served entirely in one rack's memory without cross-node memory spill.

In a cluster of 8×B200 HGX nodes, cross-node communication (gradient all-reduce, KV cache synchronization, MoE expert routing) travels over InfiniBand at 400 Gb/s (50 GB/s). The GB200 NVL72's NVLink fabric connects all 72 GPUs at 130 TB/s all-to-all — approximately 2,600x faster. For 200B+ parameter models where all-reduce can consume 40% of training step time, this translates directly into faster training at lower effective cost-per-step.

Direct Liquid Cooling (DLC) is required for the GB200 NVL72. The rack draws 120–132 kW under full load — far beyond what air cooling can handle at this density. Cyfuture AI's data center facilities include liquid-cooled infrastructure designed for high-density NVIDIA Blackwell deployments.

The NVIDIA GB200 NVL72 price varies by configuration, commitment term, and access model. Cloud providers offer GB200 NVL72 capacity ranging from approximately $10.50 to $27 per GPU-equivalent per hour (CoreWeave to Azure, as of early 2026). For competitive, transparent pricing on GB200 NVL72 GPU Server access through Cyfuture AI, contact our sales team directly for a customized quote based on your workload requirements and commitment timeline.

Yes. Cyfuture AI enables access to GB200 NVL72 capacity at the Superchip level (2 B200 GPUs + 1 Grace CPU) as well as partial and full rack configurations, allowing you to scale access to match your actual workload requirements rather than committing to full rack capacity immediately.

The GB200 NVL72 supports all major AI frameworks including PyTorch, TensorFlow, JAX, and NVIDIA NeMo. It is also compatible with NVIDIA TensorRT-LLM, Triton Inference Server, vLLM, DeepSpeed, Megatron-LM, and the full CUDA-X library stack. NVIDIA AI Enterprise software suite is supported for production enterprise deployments.

The GB300 NVL72 is the next-generation successor to the GB200 NVL72, featuring NVIDIA Blackwell Ultra GPUs with 288 GB HBM3e per GPU. The GB300 NVL72 delivers up to 50x overall AI factory performance improvement compared to Hopper-based platforms and is purpose-built for test-time scaling inference and AI reasoning tasks. The GB200 NVL72 remains widely available and is the current generation rack-scale Blackwell platform.

Unlock Exascale AI with NVIDIA GB200 NVL72

From 200B+ model training to real-time 671B inference, Cyfuture AI delivers the compute, cooling, and expertise needed to run the most demanding AI workloads at scale.

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