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AI-Dedicated Hardware

Summary of AI-Dedicated Equipment and Their External Network Connectivity

Detailed Breakdown of External Network Connectivity

1. NVIDIA DGX GH200 Supercomputer

• External Network Interfaces:
• Ethernet Ports:
  • Type: QSFP28 (Quad Small Form-factor Pluggable 28)
  • Number: 4 ports
  • Bandwidth: Each port supports up to 100 Gbps, totaling 400 Gbps.
• InfiniBand Ports:
  • Type: HDR (High Data Rate) InfiniBand
  • Number: 2 ports
  • Bandwidth: Each port supports up to 200 Gbps, totaling 400 Gbps.
• Usage: The DGX GH200 is designed for large-scale AI model training, offering robust external connectivity to integrate seamlessly into high-performance networks.

2. NVIDIA H100 Tensor Core GPU

• External Network Interfaces: (Dependent on host server configuration)
• Ethernet Ports:
  • Type: QSFP28
  • Number: Typically 4 ports per server
  • Bandwidth: Each port supports up to 100 Gbps.
• Note: The actual number and type of external network interfaces depend on the specific server chassis housing the H100 GPUs.
• Usage: Optimized for AI training and inference within server environments, providing high-speed external connectivity to support intensive computational tasks.

3. Google TPU v5

• External Network Interfaces:
• Ethernet Ports:
  • Type: QSFP28
  • Number: 8 ports per TPU v5 unit
  • Bandwidth: Each port supports up to 100 Gbps, totaling 800 Gbps.
• Custom Fabric Ports:
  • Integrated with Google’s proprietary high-speed fabric for TPU interconnects.
• Usage: Designed for cloud-based large-scale AI model training, the TPU v5 offers extensive external connectivity to handle massive data flows efficiently.

4. AMD MI300X

• External Network Interfaces:
• Ethernet Ports:
  • Type: QSFP28
  • Number: 4 ports
  • Bandwidth: Each port supports up to 100 Gbps, totaling 400 Gbps.
• InfiniBand Ports:
  • Type: HDR InfiniBand
  • Number: 2 ports
  • Bandwidth: Each port supports up to 200 Gbps, totaling 400 Gbps.
• Usage: The MI300X accelerators are ideal for both AI training and inference, providing high-speed external connections to support distributed computing environments.

5. Intel Gaudi2 AI Processor

• External Network Interfaces:
• Ethernet Ports:
  • Type: QSFP28
  • Number: 10 ports
  • Bandwidth: Each port supports up to 100 Gbps, totaling 1 Tbps.
• PCIe Slots:
  • Type: PCIe 5.0
  • Number: 2 slots
  • Bandwidth: Each slot supports up to 64 Gbps, facilitating high-speed data transfers between CPU and accelerators.
• Usage: Optimized for distributed AI training, the Gaudi2 processors provide extensive external connectivity to ensure efficient data movement across AI clusters.

6. Cerebras CS-2

• External Network Interfaces:
• Ethernet Ports:
  • Type: QSFP28
  • Number: 4 ports
  • Bandwidth: Each port supports up to 100 Gbps, totaling 400 Gbps.
• Custom Interconnect Ports:
  • Type: Cerebras Swarm Fabric
  • Number: 2 ports
  • Bandwidth: Proprietary high-speed connections for internal communication within the Cerebras ecosystem.
• Usage: The CS-2 is tailored for deep learning model training and inference, offering robust external connectivity to integrate into high-performance AI infrastructures.

7. Graphcore Bow Pod256

• External Network Interfaces:
• Ethernet Ports:
  • Type: QSFP28
  • Number: 8 ports
  • Bandwidth: Each port supports up to 100 Gbps, totaling 800 Gbps.
• InfiniBand Ports:
  • Type: HDR InfiniBand
  • Number: 4 ports
  • Bandwidth: Each port supports up to 200 Gbps, totaling 800 Gbps.
• Usage: Designed for extreme AI workloads, the Bow Pod256 offers extensive external connectivity to handle highly parallelized AI model training and inference tasks efficiently.

8. Tesla Dojo Supercomputer

• External Network Interfaces:
• Ethernet Ports:
  • Type: QSFP-DD (Quad Small Form-factor Pluggable Double Density)
  • Number: 10 ports
  • Bandwidth: Each port supports up to 400 Gbps, totaling 4 Tbps.
• Custom Interconnect Ports:
  • Type: Dojo Fabric
  • Number: 4 ports
  • Bandwidth: Proprietary high-speed connections for internal communication within the Dojo system.
• Usage: Specifically designed for autonomous driving AI training, the Dojo Supercomputer provides ultra-high external connectivity to support Tesla’s massive data and computational requirements.

Key Connectivity Features and Considerations

Interface Types

1. QSFP28 (Quad Small Form-factor Pluggable 28):

• Bandwidth: Supports up to 100 Gbps per port.
• Use Case: Commonly used for high-speed Ethernet connections in AI equipment, providing a balance between speed and density.

1. QSFP-DD (Quad Small Form-factor Pluggable Double Density):

• Bandwidth: Supports up to 400 Gbps per port.
• Use Case: Utilized in environments requiring extremely high bandwidth, such as the Tesla Dojo Supercomputer.

1. InfiniBand HDR (High Data Rate):

• Bandwidth: Supports up to 200 Gbps per port.
• Use Case: Preferred for low-latency, high-throughput interconnects in HPC and AI clusters.

1. Custom Fabric Ports (e.g., Cerebras Swarm Fabric, Dojo Fabric):

• Bandwidth: Varies based on proprietary technology.
• Use Case: Designed for optimized internal communication within specific AI infrastructures, offering high-speed data transfer capabilities.

1. PCIe 5.0:

• Bandwidth: Up to 64 Gbps per slot.
• Use Case: Facilitates high-speed communication between CPUs, GPUs, and other peripherals within servers and AI accelerators.

Number of Interfaces

• The number of external network interfaces varies significantly based on the equipment and its intended use case. High-end AI supercomputers and accelerators often feature multiple high-bandwidth ports to ensure ample connectivity for data-intensive tasks.

Bandwidth Considerations

• High Bandwidth Needs: AI workloads, especially those involving large-scale model training and distributed computing, require substantial bandwidth to handle massive data transfers efficiently.
• Low Latency Requirements: Minimizing latency is critical for real-time AI applications and for maintaining synchronization across distributed AI tasks.

Integration with Existing Infrastructure

• Scalability: Ensure that the chosen equipment can scale with your AI infrastructure needs without necessitating significant reconfiguration.
• Compatibility: Verify that the external network interfaces are compatible with your existing networking hardware and protocols to facilitate seamless integration.

Redundancy and High Availability

• Multiple Ports: Utilizing multiple network ports can provide redundancy, ensuring that AI workloads continue to operate smoothly even if one connection fails.
• Failover Mechanisms: Implement failover strategies to automatically reroute traffic in case of network disruptions, maintaining high availability for critical AI operations.

Conclusion

The latest AI-dedicated equipment offers a range of high-speed external network interfaces designed to meet the demanding requirements of modern AI workloads. Whether it’s the ultra-high bandwidth provided by QSFP-DD ports in Tesla’s Dojo Supercomputer or the low-latency InfiniBand connections in NVIDIA’s DGX GH200, each piece of equipment is tailored to ensure efficient data movement and robust connectivity within AI infrastructures.

When selecting AI hardware, consider the following:

1. Bandwidth Requirements: Assess the data throughput needs of your AI applications to choose equipment with sufficient external network interfaces.
2. Latency Sensitivity: For real-time or highly synchronized AI tasks, prioritize equipment with low-latency network interfaces like InfiniBand HDR.
3. Scalability and Flexibility: Opt for hardware that allows easy expansion of network connectivity as your AI workloads grow.
4. Compatibility and Integration: Ensure that the network interfaces are compatible with your existing infrastructure and support necessary protocols and standards.
5. Redundancy and Reliability: Implement redundant network connections to maintain high availability and prevent downtime in critical AI operations.

By carefully evaluating these factors and leveraging the detailed connectivity options provided by the latest AI-dedicated equipment, you can build a robust, high-performance AI infrastructure capable of handling the most demanding workloads.

If you need further assistance in selecting specific equipment based on your infrastructure or have additional questions about integrating these devices into your network, feel free to ask!