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The NVIDIA B200 is the flagship Blackwell GPU, representing the most powerful AI accelerator ever created. With 192GB of HBM3e memory delivering 8 TB/s bandwidth and up to 4x the performance of H100 for LLM workloads, the B200 defines the new frontier of AI computing. For CUDA developers, the B200 combines two Blackwell dies in a single GPU package, offering unprecedented compute density. The 2nd generation Transformer Engine with native FP4 support, combined with the revolutionary decompression engine, enables training and inference of frontier AI models with exceptional efficiency. This guide covers the B200's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing performance in your GPU kernels.
| Architecture | Blackwell (GB202) |
| CUDA Cores | 20,480 |
| Tensor Cores | 640 |
| Memory | 192GB HBM3e |
| Memory Bandwidth | 8,000 GB/s |
| Base / Boost Clock | 1200 / 2100 MHz |
| FP32 Performance | 90 TFLOPS |
| FP16 Performance | 2500 TFLOPS |
| L2 Cache | 96MB |
| TDP | 1000W |
| NVLink | Yes |
| MSRP | $40,000+ |
| Release | Q2 2024 |
This code snippet shows how to detect your B200, check available memory, and configure optimal settings for the Blackwell (GB202) architecture.
import torch
import pynvml
# Check if B200 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# B200 Memory: 192GB - Optimal batch sizes
# Architecture: Blackwell (GB202)
# CUDA Cores: 20,480
# Memory-efficient training for B200
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Blackwell (GB202)
torch.backends.cudnn.allow_tf32 = True
# Check available memory
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
info = pynvml.nvmlDeviceGetMemoryInfo(handle)
print(f"Free memory: {info.free / 1024**3:.1f} GB / 192 GB total")
# Recommended batch size calculation for B200
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (192 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for B200: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| LLaMA-70B Inference (tokens/sec) | 8,000 | 4x faster than H100 |
| GPT-4 Class Training | 3.5x H100 | Dramatic efficiency gains |
| Falcon-180B Single GPU | Fits in memory | H100 requires 3+ GPUs |
| Memory Bandwidth (TB/s) | 7.6 | 95% efficiency |
| FP4 Tensor TFLOPS | 10,000 | Industry leading |
| Multi-GPU Scaling | 98% | Near-perfect with NVLink |
| Use Case | Rating | Notes |
|---|---|---|
| Frontier AI Training | Excellent | The definitive choice for training largest models |
| LLM Inference | Excellent | 4x H100 performance, massive batch sizes |
| Multi-Modal AI | Excellent | 192GB handles any model architecture |
| Scientific Computing | Excellent | Exceptional FP64 and memory for simulations |
| Real-time Inference | Excellent | Lowest latency for production |
| Cost-sensitive Workloads | Poor | Extremely expensive |
The B200 uses a dual-die design with 192GB HBM3e vs 180GB, 8 TB/s bandwidth vs 5.5 TB/s, and roughly 30% more compute. It is the flagship Blackwell product for maximum performance.
The B200 significantly accelerates training of frontier models. While GPT-4 scale still requires multiple GPUs, a cluster of B200s can train such models 3-4x faster than equivalent H100 clusters.
The B200 requires liquid cooling for its 1000W TDP. It is designed for purpose-built AI datacenters with advanced thermal management infrastructure.
Despite the high upfront cost, B200 offers better TCO for large-scale AI workloads due to 4x performance improvement. The cost per token/inference is significantly lower than H100.
Lower cost Blackwell, 180GB
Proven Hopper, 141GB, lower cost
Mature ecosystem, much lower cost
192GB HBM3, competitive pricing
Ready to optimize your CUDA kernels for B200? Download RightNow AI for real-time performance analysis.