Deploy Blackwell-powered AI infrastructure at enterprise scale with proper architecture and management.

Multi-GPU Configurations

DGX GB200 System

• 36 Blackwell GPUs in single chassis
• 1.4 exaFLOPS AI performance
• 6.5TB total GPU memory
• Liquid-cooled for power efficiency
• Ideal for training foundation models

HGX B200 Server

• 8 Blackwell GPUs per node
• NVLink Switch System connecting all GPUs
• Scale to 256 GPUs in single training job
• Standard rack-mounted form factor

Cloud Deployment

AWS EC2 P6 Instances

# Launch Blackwell-powered instance
aws ec2 run-instances \
  --instance-type p6.48xlarge \
  --ami-id ami-blackwell-deep-learning \
  --key-name my-key \
  --security-group-ids sg-xxxx \
  --subnet-id subnet-xxxx

# 8x B200 GPUs, 2TB system RAM, 30TB local NVMe

Google Cloud A4 VMs

• Blackwell GPUs available in A4 instances
• Integrated with Vertex AI platform
• Dynamic GPU allocation for cost optimization
• Multi-region availability

Performance Monitoring

NVIDIA DCGM (Data Center GPU Manager)

# Monitor Blackwell GPU utilization
dcgmi dmon -e 155,156,157,203,204 -c 1000

# GPU Utilization metrics
# 155: GPU utilization
# 156: Memory utilization  
# 157: Temperature
# 203: Power usage
# 204: NVLink bandwidth

Cost Optimization

• Use spot instances for non-critical training
• Right-size GPU allocation per workload
• Leverage FP4 inference for cost reduction
• Implement auto-scaling based on demand

Best Practices

• Reserve Blackwell for LLMs >70B parameters
• Use FP8 for training, FP4 for inference
• Maximize NVLink utilization in multi-GPU setups
• Monitor power consumption (700W TDP per GPU)
• Implement proper cooling infrastructure