NVIDIA B200 vs GB200: Comprehensive Efficiency Benchmark Comparison
In the latest generation of NVIDIA's Blackwell architecture, we see two distinct accelerator models: the B200-SXM-180GB and the GB200 (part of the GB200 NVL72 system). This analysis compares their real-world performance and efficiency across various system configurations based on MLPerf Training v5.0 benchmark results.
Executive Summary
After analyzing benchmark data from dozens of system configurations ranging from single nodes to massive clusters with thousands of accelerators submitted to MLPerf Training v5.0, the results are clear:
- Average B200 Efficiency: 0.979 (latency per accelerator)
- Average GB200 Efficiency: 0.690 (latency per accelerator)
- Efficiency Ratio (B200 to GB200): 1.42
Key Finding: GB200 systems are approximately 42% more efficient than B200 systems on average, requiring significantly less latency per accelerator to complete the same workload.
Data Source: All benchmark results are from MLCommons MLPerf Training v5.0, which introduced the new Llama 3.1 405B benchmark and received over 201 performance results from 20 submitting organizations.
Understanding the Metrics
In this comparison:
- Latency is measured in minutes
- Efficiency is calculated as:
Average Latency / Total Accelerators - Lower efficiency values are better (indicating less latency per accelerator)
Top Performing Systems by Efficiency
Here are the most efficient systems tested, regardless of accelerator type:
| Rank | System Name | Accelerator | Total GPUs | Avg. Latency (min) | Efficiency |
|---|---|---|---|---|---|
| 1 | Tyche (8x NVIDIA GB200 NVL72) | GB200 | 512 | 0.56 | 0.00109 |
| 2 | Tyche (8x NVIDIA GB200 NVL72) | GB200 | 512 | 1.039 | 0.00203 |
| 3 | Tyche (2x NVIDIA GB200 NVL72) | GB200 | 144 | 1.128 | 0.00783 |
| 4 | Carina (39x NVIDIA GB200 NVL72) | GB200 | 2,496 | 27.335 | 0.01095 |
| 5 | Carina (32x NVIDIA GB200 NVL72) | GB200 | 2,048 | 32.629 | 0.01593 |
Notable: All top 5 positions are held by GB200-based systems, showcasing the architecture's superior efficiency at scale.
Complete System Rankings
Ultra-Efficient Systems (Efficiency < 0.05)
These systems demonstrate exceptional efficiency, with GB200 NVL72 configurations dominating:
| System Name | Accelerator | Total GPUs | Efficiency |
|---|---|---|---|
| Tyche (8x NVIDIA GB200 NVL72) | GB200 | 512 | 0.00109 |
| Tyche (8x NVIDIA GB200 NVL72) | GB200 | 512 | 0.00203 |
| Tyche (2x NVIDIA GB200 NVL72) | GB200 | 144 | 0.00783 |
| Carina (39x NVIDIA GB200 NVL72) | GB200 | 2,496 | 0.01095 |
| Carina (32x NVIDIA GB200 NVL72) | GB200 | 2,048 | 0.01593 |
| BM.GPU.GB200.8 | GB200 | 72 | 0.02219 |
| SRS-GB200-NVL72-M1 (18x ARS-121GL-NBO) | GB200 | 72 | 0.02293 |
| Tyche (1x NVIDIA GB200 NVL72) | GB200 | 72 | 0.02328 |
| Carina (24x NVIDIA GB200 NVL72) | GB200 | 1,536 | 0.02765 |
| 16xXE9712x4GB200 | GB200 | 64 | 0.02905 |
| BM.GPU.B200.8 | B200-SXM-180GB | 64 | 0.03155 |
| SRS-GB200-NVL72-M1 (18x ARS-121GL-NBO) | GB200 | 72 | 0.03294 |
| Tyche (1x NVIDIA GB200 NVL72) | GB200 | 72 | 0.03432 |
| 16xXE9712x4GB200 | GB200 | 64 | 0.04259 |
| BM.GPU.B200.8 | B200-SXM-180GB | 64 | 0.04350 |
High-Efficiency Systems (Efficiency 0.05 - 0.15)
| System Name | Accelerator | Total GPUs | Efficiency |
|---|---|---|---|
| Carina (16x NVIDIA GB200 NVL72) | GB200 | 1,024 | 0.06061 |
| 9xXE9680Lx4GB200 | GB200 | 36 | 0.08331 |
| 8xXE9712x4GB200 | GB200 | 32 | 0.09803 |
| 9xXE9680Lx4GB200 | GB200 | 36 | 0.11969 |
| 8xXE9712x4GB200 | GB200 | 32 | 0.13588 |
Standard Efficiency Systems (Efficiency 0.15 - 1.0)
| System Name | Accelerator | Total GPUs | Efficiency |
|---|---|---|---|
| Carina (8x NVIDIA GB200 NVL72) | GB200 | 512 | 0.23651 |
| Tyche (8x NVIDIA GB200 NVL72) | GB200 | 512 | 0.23781 |
| AS-4126GS-NBR-LCC_N2 | B200-SXM-180GB | 16 | 0.37981 |
| 4xXE9712x4GB200 | GB200 | 16 | 0.38981 |
| 4xXE9712x4GB200 | GB200 | 16 | 0.47763 |
| Tyche (4x NVIDIA GB200 NVL72) | GB200 | 256 | 0.93881 |
Single-Node / Small Cluster Systems (Efficiency > 1.0)
These are typically 4-8 GPU systems or smaller configurations:
| System Name | Accelerator | Total GPUs | Efficiency |
|---|---|---|---|
| BM.GPU.GB200.4 | GB200 | 8 | 1.375 |
| Tyche (1x NVIDIA GB200 NVL72) | GB200 | 8 | 1.393 |
| SYS-422GA-NBRT-LCC | B200-SXM-180GB | 8 | 1.401 |
| AS-A126GS-TNBR | B200-SXM-180GB | 8 | 1.406 |
| G893-SD1 | B200-SXM-180GB | 8 | 1.408 |
| Nyx (1x NVIDIA DGX B200) | B200-SXM-180GB | 8 | 1.409 |
| Lambda-1-Click-Cluster_B200_n1 | B200-SXM-180GB | 8 | 1.414 |
| ThinkSystem SR780a V3 with 8x B200 | B200-SXM-180GB | 8 | 1.416 |
| 1xXE9680Lx8B200-SXM-180GB | B200-SXM-180GB | 8 | 1.416 |
| SYS-A21GE-NBRT | B200-SXM-180GB | 8 | 1.417 |
| BM.GPU.B200.8 | B200-SXM-180GB | 8 | 1.422 |
| AS-4126GS-NBR-LCC_N1 | B200-SXM-180GB | 8 | 1.424 |
| AS-A126GS-TNBR_N1 | B200-SXM-180GB | 8 | 1.468 |
| Tyche (1x NVIDIA GB200 NVL72) | GB200 | 8 | 1.607 |
| SYS-422GA-NBRT-LCC | B200-SXM-180GB | 8 | 1.628 |
| G893-SD1 | B200-SXM-180GB | 8 | 1.638 |
| SYS-A21GE-NBRT | B200-SXM-180GB | 8 | 1.646 |
| AS-A126GS-TNBR | B200-SXM-180GB | 8 | 1.649 |
| BM.GPU.B200.8 | B200-SXM-180GB | 8 | 1.739 |
| 1xXE9680Lx8B200-SXM-180GB | B200-SXM-180GB | 8 | 1.752 |
| Nyx (1x NVIDIA DGX B200) | B200-SXM-180GB | 8 | 1.760 |
| ThinkSystem SR780a V3 with 8x B200 | B200-SXM-180GB | 8 | 1.781 |
| BM.GPU.GB200.4 | GB200 | 4 | 5.529 |
| 1xXE9712x4GB200 | GB200 | 4 | 6.358 |
Key Insights
1. GB200 Dominates Large-Scale Deployments
The GB200 NVL72 architecture excels in large cluster configurations:
- Tyche systems with 512 GPUs achieve efficiency as low as 0.00109
- Carina systems scale up to 2,496 GPUs while maintaining competitive efficiency (0.01095)
- Multi-node GB200 NVL72 configurations consistently outperform equivalent B200 setups
2. NVLink Advantage
The GB200 NVL72 systems leverage NVIDIA NVLink technology for superior interconnect bandwidth:
- 1.8 TB/s bidirectional bandwidth per GPU (fifth-generation NVLink)
- 130 TB/s total NVLink bandwidth across the 72-GPU NVL72 rack
- Enables efficient scaling across multiple nodes
- Critical for large language model training and inference
Source: NVIDIA GB200 NVL72 Specifications
3. Single-Node Performance
For 8-GPU systems, the performance gap narrows:
- B200 systems: Efficiency ranges from 1.401 to 1.781
- GB200 systems: Efficiency ranges from 1.375 to 1.607
- Difference of approximately 10-15% in single-node configurations
4. Best B200 Configuration
The highest-performing B200 system in our benchmarks:
- BM.GPU.B200.8 with 64 GPUs
- Efficiency: 0.03155
- Competitive with mid-tier GB200 configurations
Architectural Differences
GB200 (Grace Blackwell Superchip)
- Integration: CPU + GPU in one package (1 Grace CPU : 2 Blackwell GPUs per superchip)
- GPU Memory: Up to 186GB HBM3e per GPU
- GPU Memory Bandwidth: 8 TB/s per GPU (16 TB/s per superchip)
- CPU: 72 Arm Neoverse V2 cores per superchip
- CPU Memory: Up to 480GB LPDDR5X (512 GB/s bandwidth)
- GPU-to-GPU Interconnect: NVLink 5 (1.8 TB/s bidirectional)
- CPU-GPU Interconnect: NVLink-C2C (900 GB/s bidirectional)
- Design: Purpose-built for NVL72 racks (36 Grace CPUs + 72 Blackwell GPUs)
- Target: Large-scale AI training and inference, trillion-parameter models
Source: NVIDIA GB200 NVL72 Official Specifications
B200-SXM-180GB
- Integration: GPU-only, paired with x86 or Arm CPUs
- GPU Memory: 180GB HBM3e per GPU
- Memory Bandwidth: 8 TB/s per GPU
- GPU-to-GPU Interconnect: NVLink 5 (1.8 TB/s bidirectional)
- Host Interconnect: PCIe Gen5
- Design: Flexible deployment in standard servers (4 or 8 GPU configurations typical)
- Target: Versatile AI workloads, HPC, flexible infrastructure
Source: NVIDIA HGX B200 Platform Specifications
Use Case Recommendations
Choose GB200 NVL72 when:
- Building large-scale AI clusters (100+ GPUs)
- Training foundation models (LLMs, multimodal)
- Requiring maximum interconnect bandwidth
- Efficiency is critical for TCO
- Power budget allows for integrated systems
Choose B200-SXM when:
- Deploying single-node or small clusters (8-32 GPUs)
- Need flexibility in CPU choice
- Retrofit existing infrastructure
- Budget-conscious deployments
- Mixed workload environments (AI + HPC)
Efficiency by Accelerator Count
Interesting patterns emerge when grouping by GPU count:
- 4 GPUs: GB200 averages 5.944 vs B200 (no data)
- 8 GPUs: GB200 averages 1.560 vs B200 averages 1.507 (B200 slightly ahead)
- 16 GPUs: GB200 averages 0.434 vs B200 averages 0.380 (B200 competitive)
- 32-72 GPUs: GB200 dominates with efficiency < 0.10
- 512+ GPUs: GB200 exclusively, efficiency < 0.25
Conclusion
The benchmark data conclusively demonstrates that GB200 systems deliver superior efficiency, particularly at scale. With an average efficiency advantage of 42% over B200 systems, the GB200 architecture justifies its premium for large-scale AI infrastructure.
However, B200 systems remain highly competitive for:
- Small to medium deployments (up to 64 GPUs)
- Environments requiring flexible CPU selection
- Budget-constrained projects
- Mixed HPC and AI workloads
For organizations building next-generation AI infrastructure, the choice depends on:
- Scale: GB200 for 100+ GPU clusters
- Workload: GB200 for pure AI, B200 for mixed
- Budget: B200 offers lower entry cost
- Efficiency Requirements: GB200 for maximum performance/watt
As NVIDIA's Blackwell architecture continues to mature, we expect both product lines to see optimizations, but the fundamental architectural advantages of the integrated Grace Blackwell design will likely maintain GB200's efficiency lead in large-scale deployments.
About This Analysis
This benchmark comparison is based on real-world performance data from MLPerf Training v5.0, the industry-standard benchmark suite for measuring AI training performance. Results include submissions from 20 organizations including AMD, NVIDIA, Oracle, Dell Technologies, Google Cloud, Hewlett Packard Enterprise, IBM, Lenovo, Supermicro, and others.
Benchmark Details
- Benchmark Suite: MLPerf Training v5.0
- Primary Workload: Llama 3.1 405B large language model pretraining
- Metric: Wall clock time to train model to target quality (reported as latency in minutes)
- Methodology: Multiple runs with lowest/highest results discarded, remaining results averaged
- Result Count: 201 performance results from 20 submitting organizations
- Publication Date: June 2025
Data Sources & References
- MLPerf Training v5.0 Results: https://mlcommons.org/benchmarks/training/
- MLPerf Training v5.0 Announcement: https://mlcommons.org/2025/06/mlperf-training-v5-0-results/
- NVIDIA GB200 NVL72 Specifications: https://www.nvidia.com/en-us/data-center/gb200-nvl72/
- NVIDIA HGX Platform Specifications: https://www.nvidia.com/en-us/data-center/hgx/
- NVIDIA Blackwell Architecture: https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/
For more information on NVIDIA interconnect technologies, see our article on NVIDIA NVLink.
Author: flozi00 | Published: 28. Oktober 2025