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The NVIDIA B100 introduces the Blackwell architecture to datacenter AI, delivering up to 2.5x the performance of H100 for LLM inference workloads. With 180GB of HBM3e memory and revolutionary second-generation Transformer Engine, the B100 sets a new standard for AI infrastructure. For CUDA developers, Blackwell brings significant architectural improvements including a new decompression engine for handling compressed data, enhanced Tensor Cores, and native FP4 precision support. The B100 targets the sweet spot between the flagship B200 and existing H100 installations. This guide covers the B100's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing performance in your GPU kernels.
| Architecture | Blackwell (GB100) |
| CUDA Cores | 18,432 |
| Tensor Cores | 576 |
| Memory | 180GB HBM3e |
| Memory Bandwidth | 5,500 GB/s |
| Base / Boost Clock | 1200 / 2100 MHz |
| FP32 Performance | 77 TFLOPS |
| FP16 Performance | 2250 TFLOPS |
| L2 Cache | 64MB |
| TDP | 700W |
| NVLink | Yes |
| MSRP | $35,000+ |
| Release | Q2 2024 |
This code snippet shows how to detect your B100, check available memory, and configure optimal settings for the Blackwell (GB100) architecture.
import torch
import pynvml
# Check if B100 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# B100 Memory: 180GB - Optimal batch sizes
# Architecture: Blackwell (GB100)
# CUDA Cores: 18,432
# Memory-efficient training for B100
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Blackwell (GB100)
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 / 180 GB total")
# Recommended batch size calculation for B100
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (180 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for B100: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| LLaMA-70B Inference (tokens/sec) | 5,500 | 2.5x faster than H100 |
| GPT-4 Class Inference | 2,800 tokens/sec | Single GPU capable |
| Training Throughput | 2.2x H100 | Massive efficiency gains |
| Memory Bandwidth (TB/s) | 5.2 | 95% efficiency |
| FP4 Tensor TFLOPS | 9,000 | New precision tier |
| Multi-GPU Scaling | 95% | Near-linear with NVLink 5 |
| Use Case | Rating | Notes |
|---|---|---|
| LLM Inference | Excellent | 2.5x faster than H100, FP4 precision |
| LLM Training | Excellent | 180GB fits larger models per GPU |
| Generative AI | Excellent | Optimal for production AI services |
| Scientific Computing | Excellent | Enhanced FP64 for simulations |
| Real-time AI | Excellent | Lowest latency inference |
| Edge Inference | Poor | 700W not suitable for edge |
The B100 is approximately 2.5x faster for LLM inference due to the new Blackwell architecture, 180GB vs 80GB memory, and FP4 precision support. It represents a generational leap in AI performance.
FP4 is a 4-bit floating-point format new to Blackwell GPUs. It provides 2x the throughput of FP8 for inference workloads with minimal accuracy loss when used with proper quantization techniques.
If you need GPUs immediately, H100 is a proven platform with mature software support. If you can wait and primarily do LLM inference, B100 offers significantly better TCO.
B100 requires CUDA 13+ and updated versions of cuDNN, TensorRT, and ML frameworks. Most major frameworks will have Day-1 support, but some optimization may be needed.
Flagship Blackwell, even more powerful
Proven Hopper, 141GB HBM3e
Previous gen, lower cost, mature ecosystem
192GB HBM3, competitive alternative
Ready to optimize your CUDA kernels for B100? Download RightNow AI for real-time performance analysis.