Wiitek Newly Releases the Most Popular 800G OSFP 2xSR4, DR4, FR4, 800G QSFP-DD 2xSR4, DR4, FR4 Optical Transceivers, it's largely used for AI Large Model Training and Machine Learning.

1. Form Factor Foundation: OSFP vs. QSFP-DD at 800G

The choice between OSFP and QSFP-DD is the first critical architectural decision, influencing thermal performance, power envelope, and forward compatibility.

• QSFP-DD (Quad Small Form-factor Pluggable Double Density): An evolution of the ubiquitous QSFP28, it maintains a similar width but adds a second row of electrical contacts to create an 8-lane interface. Its chief advantage is backward compatibility with the vast installed base of QSFP28 (100G) and QSFP56 (200G) ports. However, its narrower profile presents thermal challenges at the higher power budgets required for 800G, typically capping at ~14-16W.

• OSFP (Octal Small Form-factor Pluggable): Designed from the outset for higher speeds, the OSFP is slightly wider and deeper than QSFP-DD. This allows for a superior thermal solution, supporting power budgets of up to 21W, which is crucial for demanding coherent optics (e.g., 800ZR) and longer-reach applications. It is inherently an 8-lane electrical interface but lacks native backward compatibility with QSFP28, requiring OSFP-to-QSFP-DD adapter cables for interoperability.

Key Differentiator: For 800G, OSFP holds a thermal advantage, making it the preferred choice for hyper-scalers and AI clusters where power density is a primary concern. QSFP-DD offers a more conservative, backward-compatible path for enterprises and data centers with existing infrastructure.

2. Comparative Analysis of 800G SR8, DR4, and FR4

The suffix denotes the physical layer (PHY) specification, defining the optical interface, fiber type, and reach.

800G-SR8: The Intra-Rack Workhorse

• Product Design & Interface: Both OSFP and QSFP-DD SR8 modules house 16 optical lanes (8 transmit, 8 receive). They utilize a MPO-16 or MPO-12 connector interface.

• Chip Design Scheme: The electrical IC (Driver & TIA) interfaces with the host switch via 8x 100G PAM4 electrical lanes. The core optical engine employs 8x 100G VCSELs (Vertical-Cavity Surface-Emitting Lasers). VCSELs are low-cost, low-power lasers ideal for short reaches on Multi-Mode Fiber (MMF).

• Transmission Rate & Connection: The architecture is parallel: 8 fibers for TX and 8 for RX. Each lane operates at 100Gbps PAM4, aggregating to 800Gbps.

• Reach & Fiber Type: Uses Wide Band Multimode Fiber (WBMMF or OM5) to achieve up to 100m. OM4 fiber can be used for shorter reaches (~70m).

• Usage & AI Application: SR8 is designed for top-of-rack (ToR) to leaf switch connections and intra-cluster networking within a single data hall. Its low cost and power are key advantages. However, in AI training clusters, where compute nodes are often spread across multiple racks in a scalable fabric, the limited reach of SR8 is a significant constraint, relegating it to the most dense, hyper-concentrated deployments.

800G-DR4: The Balanced Choice for Fabric Connectivity

• Product Design & Interface: The DR4 module uses a simpler duplex LC connector. Both OSFP and QSFP-DD variants are common.

• Chip Design Scheme: This is where significant complexity is hidden. The module's DSP (Digital Signal Processor) is critical. It:

  1. Gearboxes the 8x 100G PAM4 electrical lanes from the host.

  2. Multiplexes them into 4x 200G PAM4 optical lanes using CWDM4 or similar wavelength division multiplexing.

  3. Uses 4x uncooled Distributed Feedback (DFB) lasers, each emitting a slightly different wavelength, which are combined onto a single fiber.

• Transmission Rate & Connection: A true duplex connection: one fiber for transmit, one for receive. The 800G signal is carried over 4 wavelengths in each direction.

• Reach & Fiber Type: Uses Standard Single-Mode Fiber (SMF/G.652.D) to achieve up to 500m.

• Usage & AI Application: DR4 is arguably the sweet spot for AI cluster fabric connectivity. Its 500m reach provides the flexibility to connect racks across a large data hall or even between adjacent halls. The use of duplex SMF drastically reduces fiber count and cabling complexity compared to SR8, while its uncooled lasers keep costs and power lower than FR4. It is the dominant solution for spine-leaf connections in GPU-driven networks.

800G-FR4: The Extended-Reach Campus Link

• Product Design & Interface: Similar to DR4, FR4 uses a duplex LC connector. The OSFP's superior thermal management often makes it the preferred host for FR4 due to its higher power consumption.

• Chip Design Scheme: The architecture is similar to DR4 but employs 4x cooled DWDM lasers. The cooling allows for precise wavelength control and higher output power. The DSP uses a more advanced DWDM grid (e.g., 800GHz spacing) to place the 4 wavelengths within the C-band.

• Transmission Rate & Connection: Same as DR4: duplex SMF with 4 wavelengths.

• Reach & Fiber Type: Uses Standard Single-Mode Fiber (SMF) to achieve up to 2km.

• Usage & AI Application: FR4 is designed for longer intra-data center links and campus-level interconnects. In an AI context, it is used to connect separate data center buildings or to span very large single-level facilities where DR4's 500m reach is insufficient. While more expensive and power-hungry than DR4, it eliminates the need for additional signal regeneration equipment for these longer distances.

3. Summary Table: 800G OSFP/QSFP-DD Variants at a Glance

4. Application in AI Large-Scale Model Training

The architecture of a modern AI training cluster is a "disaggregated" compute fabric, where thousands of GPUs are networked to function as a single massive computer. The network is the nervous system of this computer.

• The Fabric Imperative: AI workloads, characterized by constant all-to-all communication (AllReduce operations), are exceptionally sensitive to network latency and bandwidth. A bottleneck in the network directly translates to idle GPUs and wasted computational resources.

• Why DR4 is the De Facto AI Fabric Standard: The 800G-DR4 module strikes the optimal balance for this environment. Its 500m reach provides ample design flexibility for distributing GPU racks across a data hall to manage power and cooling. Its duplex SMF interface simplifies cabling immensely compared to the fiber "harness" of SR8. While SR8 is viable for hyper-concentrated pods, DR4's flexibility makes it the more universal choice for scalable, multi-rack clusters.

• The Role of FR4: FR4 serves as the "trunk" link for larger AI deployments. It can connect separate AI pods within a campus or link compute clusters to centralized storage and data lakes that may be located further away.

• Form Factor in AI: In the high-stakes environment of AI, where thermal management is paramount and performance is everything, the OSFP form factor is often selected for its headroom, especially for the more powerful FR4 optics and the future-proofing it offers for 1.6T and coherent (ZR) pluggables.

Conclusion

The landscape of 800G optics is not one of winners and losers, but of specialized tools for specific tasks. The 800G-SR8 provides a cost-effective solution for the shortest reaches. The 800G-DR4 has emerged as the cornerstone for building high-bandwidth, low-latency AI training fabrics, offering an ideal compromise of reach, cost, and fiber infrastructure. The 800G-FR4 extends this capability to campus-scale deployments. Underpinning this, the choice between OSFP and QSFP-DD hinges on a trade-off between thermal performance/future-proofing and backward compatibility. As AI models continue to grow in complexity, driving the insatiable demand for bandwidth, these 800G optical modules form the critical physical layer upon which the next generation of computational breakthroughs will be built.