高速互連技術為何對擴展 AI 數據中心至關重要？

As AI models become larger, a single processor or server may not be enough. AI data centers connect multiple accelerators, servers, and memory resources so they can work together, and high-speed interconnect technology lets them exchange data efficiently while managing latency, bandwidth, and power. Without efficient connections, even powerful AI processors can spend valuable time waiting for data.

1. Faster Data Transfer Between AI Accelerators
AI training and inference often move large amounts of data between processors. High-speed interconnects can enable faster communication between accelerators, more efficient sharing of model parameters, fewer communication bottlenecks, and better use of computing resources.
Why it matters: Faster processors alone don’t guarantee better system performance if the connections between them become the bottleneck.

2. Lower Latency for Distributed AI Workloads
In distributed AI computing, accelerators or servers exchange intermediate results, so communication delays can slow the whole task. Low-latency interconnects matter most for large-scale training and distributed inference.
Note: Actual latency depends on the interconnect, network topology, software, traffic levels, and distance between components.

3. Better Scalability as AI Infrastructure Expands
Adding more accelerators doesn’t guarantee proportional performance gains. An effective AI data center interconnect must keep communication efficient as the system grows. Key considerations include:
Sufficient bandwidth between servers and accelerators
Network designs that support larger deployments
Efficient routing and traffic management
Compatibility with the intended workload and software stack

4. Improved Power Efficiency Across AI Systems
Communication can account for a meaningful share of power use in large systems. Teams should evaluate not only network speed but also interconnect power consumption, energy per transferred bit, latency, and real-world throughput.

5. Supporting Advanced AI Chip and Packaging Architectures
High-speed connectivity matters both inside AI computing packages and between systems:
On-package interconnects connect compute dies and memory within advanced packages.
Chip-to-chip links enable communication between separate processing components.
Data center networks connect servers, accelerators, and storage across the infrastructure.

Networking chips and custom ASICs for data centers can integrate high-speed SerDes and other connectivity functions to move data between compute, memory, and servers. Technologies such as optical interconnects and co-packaged optics may suit different architectures, each with trade-offs in bandwidth, reach, power, cost, and complexity.

6. What Should Businesses Evaluate When Choosing Interconnect Technology?

- Bandwidth: Can it support the data volume your workload needs?
- Latency: How quickly can connected systems exchange information?
- Power efficiency: How much energy does data transfer require?
- Scalability: Can it support additional accelerators and servers?
- Reliability and compatibility: Does it work with your hardware, software, and network?
- Total cost of ownership (TCO): What are the deployment, operating, cooling, and maintenance costs?

Evaluate under realistic workloads, since peak data rates alone give an incomplete picture.

How Does MediaTek Approach High-Speed Connectivity for AI Data Centers?
MediaTek approaches connectivity as part of the full chip design, with high-speed interconnect developed alongside compute, memory, and packaging for AI data centers. Actual results depend on the implementation and infrastructure.

Final Takeaway

High-speed interconnect technology is essential for scaling AI data centers because it lets accelerators and servers exchange data efficiently. The key is to scale data movement alongside computing capacity, so adding more AI processors delivers real improvements in performance.