Data Center Interconnect: Why Must We Evolve to 800G?

The previous generation of data centers—deployed prior to the advent of AI computing—were predominantly built upon 100G networks. Within the business scenarios prevalent at the time—including virtualization, container orchestration, and distributed storage—100G was entirely sufficient, presenting no concerns regarding bandwidth bottlenecks. However, the sudden emergence of ChatGPT in 2022 marked the dawn of the "AI Era" and fundamentally transformed the landscape. The massive data demands generated by AI training proved utterly unsupportable by traditional 100G networks; this colossal demand has subsequently driven the further evolution of optical modules toward higher speeds and lower power consumption.

Over the past four years, fueled by the explosive growth of big data computing, GPU memory bandwidth has surged from 3TB/s in the H100 to 7.2TB/s in the GB200—a 2.4-fold increase. Even more remarkably, cluster interconnect bandwidth has skyrocketed from 400Gbit/s to 12.8Tbit/s—a staggering 32-fold increase. The compound annual growth rate (CAGR) of bandwidth within DCI backbone networks has reached 45%, far outpacing the growth rate of traditional internet traffic. By 2030, AI-related traffic is projected to account for 60% of total network traffic; this immense market demand continues to drive the evolution of data center network speeds toward 400G and 800G.

1. Where Do the Bottlenecks Lie in 100G Networks?

Within high-performance AI data centers, traditional 100G networks face three primary dilemmas:

1.1. The First Dilemma: Bandwidth Limitations.

Traffic within high-performance AI data centers is predominantly "East-West" traffic (internal data center communication). When executing large-parameter computational models, the volume of internal data center traffic can easily reach terabyte-scale magnitudes. When confronted with these massive data streams, a 100G network bandwidth inevitably results in traffic congestion. This congestion leads to significant computational latency—an outcome that is wholly unacceptable in the context of high-performance AI data centers. Under these circumstances, the evolution of data center networks toward 400G and 800G becomes an indispensable developmental imperative.

1.2. The Second Dilemma: High Power Consumption.

The 100G optical modules used in data centers do not differ significantly from standard optical modules; their power consumption is relatively low, typically ranging from 3.5W to 4.5W. This is substantially lower than the 10W to 20W power consumption of 400G and 800G optical modules. Consequently, for a given deployment scale, a 100G network consumes less power overall. However, high-performance AI data centers prioritize maximum network bandwidth. When comparing networks at equivalent total bandwidth levels, the power consumption of a 100G network can be several times higher than that of 400G or 800G networks. This places immense strain on the data center's power supply infrastructure and incurs significant additional operational and maintenance costs—a key driving force behind the data center industry's migration toward 400G and 800G networks.

1.3: The Third Dilemma—Low Density per Unit Space

Current standard data centers typically utilize 42U server racks. Since the physical space within these racks is fixed, only a finite number of network devices can be accommodated within this limited volume. Given this constraint, deploying 400G or 800G networks within a data center yields a density—in terms of bandwidth per unit of physical space—that is four to eight times higher than that of a 100G network. This enables the handling of significantly higher data traffic volumes without the risk of network congestion, thereby eliminating the need to physically expand the data center's footprint solely to address bandwidth shortages. After all, the cost of physical space is exceptionally high; this substantial cost factor serves as a primary catalyst driving data center operators to transition toward 400G and 800G network infrastructures.

2. 400G and 800G Networks: The Main Battleground for Data Centers

As of 2026, 400G networks have already become the foundational network configuration for high-performance AI data centers. Furthermore, 800G data centers are currently being constructed in volume and are steadily capturing an increasing share of the data center networking market. At this very moment, an analysis of the global optical module market by data rate reveals that 400G modules command a dominant share of 58.5%, while the market share for 800G modules has rapidly surged to 40.7%; meanwhile, 1.6T modules are also beginning to emerge on the horizon. The value that 400G and 800G networks deliver to high-performance AI data centers extends far beyond the mere doubling of data transmission speeds. From an architectural perspective, higher bandwidth enables the construction of large-scale clusters with fewer switching layers. This facilitates the creation of flatter, lower-latency switching networks—reducing the number of switching tiers and intermediate hops—thereby significantly lowering end-to-end latency. This paves a broader and more robust path for the full-scale arrival of the AI ​​era, accelerating AI’s integration into households and daily life, and ushering in a more convenient and brighter future for us all.

Conclusion

For high-performance AI data centers, the deployment of 400G and 800G technologies represents a future that data center operators simply cannot afford to ignore. Amidst the surging tide of the AI ​​era, falling behind means obsolescence—and the inability to recoup initial investments. As every AI company vies for its ticket to entry into this new era, the "arms race" in optical communications is set to continue—unabated and into the foreseeable future. YXFiber specializes in providing one-stop 400G and 800G networking solutions tailored specifically for high-performance AI data centers; backed by industry-leading product quality, we empower data center networks to reach new heights.