4X Rtx6000 Pro 2X L40S 2X Rtx6000 Ada

4X Rtx6000 Pro 2X L40S 2X Rtx6000 Ada: The Ultimate Homelab Workstation Breakdown

1. Introduction: The New Frontier of Personal High-Performance Computing

The phrase “personal supercomputer” once sounded like science fiction, but today’s DevOps engineers and system administrators are building workstation setups that outperform yesterday’s datacenter racks. The configuration described in our title - 4x NVIDIA RTX6000 Pro, 2x L40S, and 2x RTX6000 Ada GPUs combined with dual AMD EPYC 9754 processors and 1.5TB of DDR5 RAM - represents a new class of homelab infrastructure that blurs the line between personal workstations and enterprise computing clusters.

For DevOps professionals working with:

• Machine learning workloads
• Large-scale simulations
• GPU-accelerated CI/CD pipelines
• High-performance containerized workloads

This class of hardware enables unprecedented local development capabilities. The thermal management challenge alone (handled here by a dedicated 10.8 kW MRCOOL system) demonstrates how far personal computing has evolved.

In this comprehensive guide, we’ll examine:

• Hardware selection rationale for mixed GPU environments
• Infrastructure management challenges at this scale
• Performance optimization techniques
• Real-world DevOps applications for such configurations
• Operational considerations for maintainability

2. Understanding the Hardware Stack

2.1 GPU Breakdown: Architectural Differences

GPU Model	Architecture	CUDA Cores	VRAM	TDP	Key Features
RTX 6000 Ada	Ada Lovelace	18,176	48GB	300W	3rd-gen RT cores
L40S	Ada Lovelace	18,176	48GB	350W	Virtualization optimized
RTX 6000 (Pro)	Ampere	10,752	48GB	300W	FP64 performance

Key observations:

1. Mixed architecture strategy: Combining Ada Lovelace (L40S/Ada) and Ampere (Pro) GPUs requires careful workload partitioning
2. Memory symmetry: All GPUs feature 48GB VRAM, simplifying memory management
3. Thermal density: 576GB total VRAM generates significant heat (10.8kW cooling capacity required)

2.2 CPU and Memory Considerations

The dual AMD EPYC 9754 processors (2x128 cores/256 threads) with 1.5TB DDR5 ECC RAM create an ideal environment for:

• GPU virtualization (vGPU/vWS)
• Memory-intensive workloads
• PCIe lane saturation (128 lanes per CPU)

2.3 Network Infrastructure

The ConnectX-7 InfiniBand adapter provides:

• 400Gb/s throughput
• RDMA capabilities
• Low-latency GPU-to-GPU communication

2.4 Storage Subsystem

The 400TB (20x20TB) file server with dual 25GbE connections enables:

• 3.2GB/s theoretical throughput
• Large dataset staging
• Distributed training datasets

3. Prerequisites for GPU-Centric Homelabs

3.1 Hardware Requirements

• Power: 240V circuit with 100A capacity (minimum)
• Cooling: 10kW+ thermal dissipation capability
• Physical space: 4U+ chassis with proper airflow
• PCIe topology: x16 slots with proper bifurcation support

3.2 Software Requirements

• OS: Ubuntu 22.04 LTS (Linux 6.5+ kernel)
• NVIDIA drivers: 550.54.14+
• CUDA toolkit: 12.3+
• Container runtime: Docker 25.0+ with NVIDIA Container Toolkit

3.3 Network Configuration

# InfiniBand basic verification
ibstat
ibv_devinfo -v

# Expected output characteristics:
#     port_guid: 0x...
#     link_layer: InfiniBand
#     max_mtu: 4096

3.4 Security Considerations

1. Physical security: BIOS/UEFI password protection
2. Network isolation: Dedicated InfiniBand subnet
3. Access control: SELinux/AppArmor profiles for GPU access

4. Installation & Configuration Walkthrough

4.1 Driver Installation (Multi-Architecture GPUs)

# Add NVIDIA driver repository
sudo apt install linux-headers-$(uname -r)
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

# Install drivers supporting both architectures
sudo apt update && sudo apt install -y nvidia-driver-550 cuda-drivers-550

4.2 CUDA Toolkit Configuration

# Verify mixed architecture support
nvidia-smi -q | grep "Architecture"
# Should show:
#    Architecture      : Ampere (for RTX6000 Pro)
#    Architecture      : Ada Lovelace (for L40S/Ada)

# Install multi-arch CUDA
wget https://developer.download.nvidia.com/compute/cuda/12.3.2/local_installers/cuda_12.3.2_545.23.08_linux.run
sudo sh cuda_12.3.2_545.23.08_linux.run --silent --toolkit --samples --override

4.3 Docker GPU Configuration

# Configure Docker daemon for NVIDIA runtime
sudo tee /etc/docker/daemon.json <<EOF
{
  "runtimes": {
    "nvidia": {
      "path": "/usr/bin/nvidia-container-runtime",
      "runtimeArgs": []
    }
  },
  "default-runtime": "nvidia"
}
EOF

# Restart Docker
sudo systemctl restart docker

4.4 GPU Workload Isolation

# Create GPU groups by architecture
nvidia-smi mig -i $GPU_ID -cgi 0 # For compute instances

5. Performance Optimization Techniques

5.1 GPU Scheduling Configuration

# Set GPU compute mode to exclusive process
nvidia-smi -i $GPU_ID -c 3

# Verify with:
nvidia-smi -q -i $GPU_ID | grep "Compute Mode"

5.2 InfiniBand Tuning

# Set RDMA parameters
sudo sysctl -w net.core.rmem_max=268435456
sudo sysctl -w net.core.wmem_max=268435456
sudo sysctl -w net.ipv4.tcp_rmem='4096 87380 268435456'

5.3 Container Runtime Configuration

# Sample Dockerfile for mixed GPU workloads
FROM nvidia/cuda:12.3.2-base-ubuntu22.04

ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility

# Architecture-specific builds
ARG TARGETARCH
RUN if [ "$TARGETARCH" = "ampere" ]; then \
      apt-get install -y libampere-optimized; \
    elif [ "$TARGETARCH" = "ada" ]; then \
      apt-get install -y libada-optimized; \
    fi

6. Operational Management

6.1 Monitoring Stack Configuration

# prometheus.yml snippet
scrape_configs:
  - job_name: 'nvidia_gpu'
    static_configs:
      - targets: ['localhost:9100']
  - job_name: 'infiniband'
    static_configs:
      - targets: ['localhost:9400']

6.2 Container Operations

# Run container with specific GPU architecture
docker run -it --gpus '"device=0,1"' \ # Ampere GPUs
           --gpus '"device=2,3,4,5"' \ # Ada GPUs
           -e NVIDIA_VISIBLE_DEVICES=all \
           nvidia/cuda:12.3.2-base

# Inspect container GPU access
docker inspect $CONTAINER_ID | grep -i nvidia

7. Troubleshooting Guide

7.1 Common Issues and Solutions

Problem: Mixed GPU architectures not detected properly
Solution: Verify driver compatibility matrix at NVIDIA Docs

Problem: PCIe bandwidth saturation
Diagnosis:

nvidia-smi -q -i $GPU_ID | grep "BAR1"
# Check Used/Total fields

Problem: Thermal throttling
Monitoring:

nvidia-smi --query-gpu=temperature.gpu --format=csv

8. Conclusion: The Future of Personal Compute Clusters

This configuration demonstrates how homelab environments are evolving to handle workloads previously reserved for cloud providers. The combination of:

• Heterogeneous GPU architectures
• High-core-count CPUs
• Low-latency networking
• Massive local storage

Creates a platform for advanced DevOps workflows including:

1. Local large language model (LLM) training
2. Computational fluid dynamics simulations
3. Multi-architecture CI/CD testing
4. Hybrid cloud development environments

Key resources for further exploration:

• NVIDIA Multi-Instance GPU Documentation
• AMD EPYC Performance Tuning Guide
• InfiniBand Performance Tuning

As hardware continues to democratize high-performance computing, DevOps engineers must adapt their infrastructure management strategies to handle these powerful systems efficiently. The true value lies not just in raw performance, but in the ability to replicate production environments locally - accelerating development cycles while reducing cloud costs.