NVIDIA GB200 Grace Blackwell

What is the NVIDIA GB200 Grace Blackwell?

The NVIDIA GB200 Grace Blackwell Superchip is NVIDIA's flagship AI computing platform that integrates two Blackwell GPUs (B200) with a Grace ARM-based CPU into a single, coherently connected system. Announced at NVIDIA GTC in March 2024, the GB200 represents a fundamental architectural advancement over previous generation GPUs. Unlike traditional systems where GPUs and CPUs communicate over PCIe, the GB200 uses NVLink-C2C (chip-to-chip) interconnect providing 900 GB/s bidirectional bandwidth between the Grace CPU and Blackwell GPUs. This tight integration eliminates bottlenecks and enables unprecedented performance for AI workloads.

The GB200's Blackwell architecture features a second-generation Transformer Engine with FP4 (4-bit floating point) precision, doubling AI training and inference performance while reducing memory and power requirements. Each Blackwell GPU contains 208 billion transistors (vs 80 billion in H100), manufactured on TSMC's 4NP process. The Grace CPU provides 72 ARM Neoverse V2 cores with 480GB LPDDR5X memory, optimized for AI data preprocessing, CPU-based inference, and managing distributed training workloads. Together, the GB200 delivers transformational improvements: 25x lower cost and energy consumption for LLM inference compared to H100, and 30x faster training for trillion-parameter models.

Technical Specifications

Blackwell GPU Architecture

• 2x B200 Blackwell GPUs per GB200 superchip
• 208 billion transistors per GPU (4NP process technology)
• 192GB HBM3e memory per GPU (384GB total per superchip)
• 8TB/s memory bandwidth per GPU
• Second-generation Transformer Engine with FP4, FP6, FP8 precision
• 20 petaFLOPS FP4 AI performance per superchip
• 10 petaFLOPS FP8 performance, 2.5x faster than H100
• NVLink 5.0 providing 1.8TB/s GPU-to-GPU bandwidth

Grace CPU and System Integration

• 72-core ARM Neoverse V2 CPU (Grace architecture)
• 480GB LPDDR5X system memory for CPU
• NVLink-C2C interconnect: 900GB/s bidirectional CPU-GPU bandwidth
• Coherent memory access between CPU and GPU
• DDR5 memory channels for high-bandwidth data access
• PCIe Gen 5 for external connectivity
• Support for Confidential Computing and secure enclaves

GB200 NVL72 Rack System

• 72 Blackwell GPUs + 36 Grace CPUs in a single rack
• Liquid-cooled for thermal efficiency
• 1.44 exaFLOPS FP4 AI performance per rack
• 130TB total GPU memory per rack
• Fifth-generation NVLink interconnect (1.8TB/s per GPU)
• BlueField-3 DPUs for networking and security offload
• InfiniBand or Ethernet networking options
• Up to 72kW power consumption per rack (vs 120kW for equivalent H100 setup)

Performance and Efficiency Gains

The GB200's performance improvements are staggering. For LLM inference (serving GPT-class models), the GB200 delivers 25x better cost and energy efficiency than H100—meaning the same inference workload that required 100 H100 GPUs can run on just 4 GB200 superchips. For training trillion-parameter models (like GPT-5 scale), GB200 provides 30x faster training than the previous generation. This is achieved through FP4 precision in the Transformer Engine, which reduces memory bandwidth requirements by 2x while maintaining model accuracy, plus the NVLink-C2C interconnect that eliminates CPU-GPU bottlenecks.

Energy efficiency is a critical advantage. A GB200 NVL72 rack consumes approximately 72kW for 1.44 exaFLOPS of AI compute, whereas achieving equivalent performance with H100 would require ~120kW. For enterprises and cloud providers running AI at scale, this 40% reduction in power consumption translates to massive operational savings and reduced carbon footprint. The liquid cooling system enables higher density deployments, allowing datacenters to pack more AI compute per square foot than air-cooled alternatives.

Use Cases and Applications

The GB200 is designed for the most demanding AI workloads:

• Training foundation models with 1+ trillion parameters (GPT-5, Claude 4 scale)
• High-throughput LLM inference serving millions of users
• Multimodal AI models processing text, images, video, and audio
• Real-time recommendation systems for e-commerce and streaming
• Agentic AI systems with complex reasoning and tool use
• Scientific computing (drug discovery, climate modeling, genomics)
• Autonomous vehicle simulation and training
• Generative AI for video and 3D content creation
• Large-scale RAG (Retrieval-Augmented Generation) systems
• Digital twin simulations for manufacturing and logistics

GB200 vs H200 and H100

Compared to NVIDIA's previous generation GPUs, the GB200 represents a generational leap. The H100 delivers ~3 petaFLOPS FP8, while GB200 delivers 20 petaFLOPS FP4—a 6.6x raw performance increase, plus additional efficiency from tighter CPU-GPU integration. The H200 (evolved H100 with 141GB HBM3e) offers incremental improvements, whereas GB200 is a complete architectural redesign. For LLM inference, GB200's 25x efficiency gain over H100 is game-changing, enabling real-time AI applications that were economically infeasible before.

The trade-off is cost and availability. GB200 systems are significantly more expensive (estimated $2-3M per NVL72 rack vs ~$300K per H100 DGX system) and require liquid cooling infrastructure. For workloads that don't require trillion-parameter models or massive-scale inference, H100 or H200 may provide better cost-effectiveness. However, for frontier AI research, hyperscale LLM deployments, or training next-generation foundation models, GB200's performance and efficiency make it indispensable.

Availability and Cloud Access

The GB200 began shipping to select customers in Q2 2025, with broader availability through Q3-Q4 2025. Major cloud providers including AWS, Azure, Google Cloud, Oracle Cloud, and Lambda Labs are deploying GB200 infrastructure. Cloud instance pricing is not yet publicly available but is expected to range from $30-50/hour for single GB200 superchips to $2000+/hour for full NVL72 rack access. Enterprise customers can purchase GB200 systems directly from NVIDIA or through partners like Dell, HPE, Lenovo, and Supermicro, with lead times of 6-12 months due to high demand.

Integration with 21medien Services

21medien provides access to GB200 infrastructure through partnerships with leading cloud providers and direct allocation agreements. We leverage GB200 for cutting-edge AI research, training custom foundation models for enterprise clients, and deploying ultra-high-performance inference services. Our team specializes in GB200 optimization—configuring distributed training across NVL72 racks, optimizing models for FP4/FP6 precision, and designing inference pipelines that maximize GB200's efficiency. We offer GB200 consulting, workload migration from H100/A100, and managed AI infrastructure services for clients requiring frontier AI capabilities.

Pricing and Access

GB200 pricing varies by deployment model. Cloud instances are estimated at $30-50/hour for single superchips (preliminary pricing), with full NVL72 rack access likely $2000-3000/hour. On-premises GB200 NVL72 racks are estimated at $2-3M per system including installation and cooling infrastructure. Lead times are 6-12 months due to high demand. Enterprise volume commitments may secure priority allocation and pricing discounts. Reserved instances and long-term contracts will likely offer 30-50% discounts vs on-demand pricing. For most organizations, cloud access through AWS, Azure, GCP, or specialized AI cloud providers (Lambda Labs, Hyperstack) will be the most practical path to GB200 infrastructure.