400G and 200G Optical Transceivers for High-Throughput AI Storage Networks

Large-scale AI training requires more than a powerful GPU compute fabric.

Modern AI clusters must also move enormous datasets between distributed storage systems and GPU servers quickly and consistently. If storage traffic competes directly with compute traffic, training performance can become less predictable and expensive GPU resources may spend more time waiting for data.

The reference architecture shown in the image addresses this challenge with a dedicated Storage Network built on a separate Spine-Leaf fabric.

The architecture uses:

• dedicated Spine switches
• dedicated Leaf switches
• 400G optical connectivity for Switch-to-Switch links
• 200G optical connectivity for NIC-to-Leaf links
• MTP/MPO fiber cabling
• ConnectX-6 / ConnectX-7-class NICs
• an RDMA-based network architecture

For companies purchasing optical modules for AI, storage, HPC or cloud infrastructure, this creates a clear physical-layer requirement for reliable 400G and 200G optical connectivity.

As a Taiwan optical transceiver manufacturer, Optech can support these network layers with high-speed optical transceivers, fiber connectivity and project-specific qualification services.

What Is an AI Storage Network?

An AI Storage Network is a dedicated network used to move training data, checkpoints and other large datasets between storage systems and GPU compute servers.

Unlike the GPU Compute Network, which focuses primarily on GPU-to-GPU communication, the Storage Network is optimized for:

Storage → GPU Server

and

GPU Server → Storage

data movement.

This separation helps reduce contention between two different traffic types:

Compute traffic

and

Storage traffic

For large AI clusters, keeping those workloads on separate fabrics can help maintain more predictable network behavior.

Why Separate Storage Traffic from the GPU Compute Fabric?

The image emphasizes that the storage network is designed to provide dedicated, high-throughput data access for AI training workloads.

This is important because AI training environments continuously access:

• training datasets
• model checkpoints
• intermediate data
• distributed file systems
• object storage
• metadata
• preprocessing data

If this traffic shares the same network resources as intensive GPU-to-GPU communication, congestion can affect both workloads.

A dedicated storage fabric can help provide:

• more predictable throughput
• reduced network contention
• greater consistency
• more stable training performance
• easier traffic engineering

Spine-Leaf Architecture for AI Storage Networks

The reference design uses a Spine-Leaf topology.

The architecture can be simplified as:

Storage / GPU Servers

↓

Leaf Switches

↓

Spine Switches

Each Leaf switch connects upward to the Spine layer, while server NICs connect to the Leaf layer.

This architecture is well suited to large-scale storage environments because it provides:

• scalable east-west bandwidth
• multiple network paths
• predictable topology
• easier capacity expansion
• high port density

400G Optical Transceivers for Switch-to-Switch Connectivity

The reference architecture uses 400G optical transceivers for Switch-to-Switch connections.

The image specifically shows a 400G QSFP-DD SR8-class optical solution.

This means the high-capacity links between Spine and Leaf switches operate at 400G.

For storage fabrics, these uplinks must carry aggregated traffic from many servers and storage nodes.

A 400G optical backbone helps provide enough bandwidth for:

• distributed storage traffic
• large dataset transfer
• AI training pipelines
• high-performance file systems
• storage cluster synchronization

Why 400G Is Important in the Spine-Leaf Layer

If multiple 200G server links feed into a Leaf switch, the uplinks must have enough capacity to avoid becoming a bottleneck.

400G optical links provide a practical way to aggregate multiple lower-speed endpoint connections while maintaining a high-throughput fabric.

For customers building AI storage networks, 400G Spine-Leaf optics can help support:

higher switch bandwidth

fewer uplink ports

better bandwidth density

simpler cabling

future growth

400G QSFP-DD SR8 for Short-Reach Storage Fabrics

The image shows a 400G QSFP-DD SR8-class optical module for Switch-to-Switch connectivity.

SR8 is typically associated with short-reach parallel multimode fiber applications.

In a storage network, this type of module can be suitable for connections within:

• the same data hall
• adjacent rows
• high-density Spine-Leaf fabrics
• GPU storage clusters
• HPC storage environments

For customers with existing multimode fiber infrastructure, SR-class connectivity can provide a practical option for short-distance 400G links.

200G Optical Transceivers for NIC-to-Leaf Connectivity

The image shows 200G optical transceivers for NIC-to-Leaf connections.

This is the server-facing portion of the storage network.

The reference architecture uses a QSFP SR4 200G-class optical solution between the NIC and Leaf switch.

This allows GPU servers or storage nodes to connect to the network at 200G while the switching fabric itself operates at 400G.

Why 200G Is Useful on the Server Side

Not every server requires an 800G network interface.

For storage access, 200G can provide a strong balance between:

• bandwidth
• NIC cost
• power consumption
• server interface density
• switch port utilization

A 200G NIC connection can provide substantial throughput for AI training datasets without requiring the server-side network to operate at the same rate as the compute fabric.

ConnectX-6 / ConnectX-7 NIC Connectivity

The image lists:

ConnectX-6 / ConnectX-7

as NIC options.

These high-speed NIC families are commonly used in HPC, storage and AI networking environments.

For an optical module supplier, this means NIC-side compatibility matters.

A 200G optical transceiver should be evaluated not only for nominal speed but also for:

• host recognition
• EEPROM compatibility
• link-up behavior
• FEC behavior
• diagnostics
• optical power
• lane stability
• thermal performance

Optech can work with customers on qualification against the actual NIC and switch platform.

RDMA-Based Storage Architecture

The image describes the Storage Network as being built on a dedicated RDMA-based architecture.

RDMA can reduce CPU involvement in data movement and improve transfer efficiency between storage systems and GPU servers.

This is especially useful in data-intensive AI workloads where large amounts of data need to move quickly between:

Storage Systems

and

GPU Servers

The optical transceiver itself does not implement RDMA, but it provides the physical Ethernet link that the RDMA network depends on.

Why the Physical Optical Layer Matters for RDMA Storage

A high-performance RDMA network still depends on reliable physical links.

Problems such as:

• high BER
• unstable optical power
• incorrect FEC behavior
• marginal signal integrity
• thermal instability
• module incompatibility

can reduce the performance or stability of the storage network.

For companies deploying AI storage fabrics, optical module quality is therefore part of overall RDMA performance.

Fiber Cabling for Switch-to-Switch Connectivity

The image shows a dedicated multimode fiber jumper for Switch-to-Switch links.

This highlights an important point:

Optical modules and fiber cabling must be designed as one link.

For 400G parallel optics, customers should consider:

• MTP/MPO connector type
• fiber polarity
• fiber count
• OM4 specification
• insertion loss
• patch panels
• trunk cables
• jumper length

Selecting the correct transceiver but the wrong fiber architecture can still result in link problems.

Fiber Breakouts for NIC-to-Leaf Connectivity

The image also shows MTP/MPO breakout fiber cables for NIC-to-Leaf links.

Breakout cabling can be useful when high-density switch ports need to connect to multiple server or NIC interfaces.

This type of architecture can help optimize:

• switch port utilization
• rack cabling
• fiber density
• deployment flexibility

Optech can work with customers on optical modules together with the required fiber connectivity.

400G Spine-Leaf + 200G Server Connectivity

The reference architecture uses a practical mixed-speed design:

400G Spine-to-Leaf

and

200G NIC-to-Leaf

This is a good example of why not every network layer needs to operate at the same speed.

The switching fabric needs enough capacity to aggregate traffic from many servers.

The endpoint layer can use the speed most appropriate for the workload.

This can provide a better balance between performance and cost.

Typical AI Storage Network Architecture

A simplified design could look like:

Distributed Storage

↓

200G NIC

↓

200G Optical Transceiver

↓

Leaf Switch

↓

400G Optical Transceiver

↓

Spine Switch

↓

Other Storage / GPU Zones

This creates a dedicated high-bandwidth path for AI training data.

Why Storage Bandwidth Matters for AI Training

AI models are becoming larger, but datasets are also becoming larger.

Training systems may need to continuously read:

• image datasets
• video datasets
• language corpora
• multimodal datasets
• vector databases
• checkpoints

If storage cannot deliver data fast enough, GPU utilization can drop.

The storage network therefore has a direct impact on overall training efficiency.

Consistent Data Access Can Improve Cluster Efficiency

The reference architecture emphasizes:

sustained bandwidth

fast response

and

reliable access to distributed datasets

These characteristics are especially important when hundreds of servers access shared storage simultaneously.

A dedicated network helps isolate storage traffic and can make performance more predictable.

Optech 400G Optical Solutions for AI Storage

Optech can support 400G optical connectivity for high-speed storage fabrics.

Possible project requirements may include:

• QSFP-DD
• SR-class optics
• DR-class optics
• FR-class optics
• multimode fiber
• single-mode fiber
• short- and medium-reach links

The correct product depends on actual network distance and fiber infrastructure.

Optech 200G Optical Solutions for Server Connectivity

For NIC-to-Leaf applications, Optech can also provide 200G optical solutions.

Depending on the platform, customers may require:

• QSFP56-class modules
• SR4
• DR4
• AOC
• DAC
• other high-speed interconnect solutions

This gives customers flexibility when choosing the physical connection between servers and Leaf switches.

200G Optical Transceiver vs DAC vs AOC

Not every NIC-to-Leaf connection needs a pluggable optical module.

For very short links, alternatives may include:

DAC

for low-cost, low-power connectivity.

AOC

for lightweight integrated optical connectivity.

Optical Transceiver + Fiber

for modularity, structured cabling and longer reach.

The correct solution depends on:

• distance
• rack layout
• power target
• serviceability
• fiber infrastructure

Why Companies Buying Storage Optics Should Consider Compatibility

In a large AI storage network, customers may deploy multiple switch and NIC platforms.

Even when interfaces are standards-based, host behavior can vary.

For this reason, customers should evaluate:

• switch recognition
• NIC recognition
• firmware compatibility
• DOM / diagnostics
• FEC
• optical power
• link stability

Optech can work with buyers on platform-specific qualification.

Optech Platform Qualification Support

For optical module projects, Optech can support evaluation according to the customer's actual environment.

This may include:

EEPROM / Module Recognition

Link-Up Test

Optical Power Test

Digital Diagnostics

Temperature Monitoring

FEC Status

Lane Operation

Compatibility Coding

Long-Term Stability

For companies planning volume deployments, sample qualification can reduce risk before mass production.

From Engineering Samples to Volume Orders

A typical project process can be:

Requirement Review

↓

Product Selection

↓

Engineering Samples

↓

Switch + NIC Compatibility Test

↓

Optical Link Validation

↓

Customer Qualification

↓

Volume Production

This allows customers to verify the complete 400G / 200G storage network link before scaling.

What Information Should Buyers Provide?

Companies sourcing optical modules for AI storage networks should ideally provide:

• Spine switch model
• Leaf switch model
• NIC model
• firmware version
• 400G or 200G requirement
• link distance
• fiber type
• connector
• sample quantity
• forecast quantity
• deployment schedule

This helps Optech recommend the appropriate configuration.

Who Needs This Type of Optical Solution?

This architecture is particularly relevant to:

• AI data centers
• GPU cloud providers
• HPC centers
• storage vendors
• server manufacturers
• cloud service providers
• data center system integrators
• optical module distributors
• research institutions
• distributed storage operators

Why Choose Optech for AI Storage Network Optics?

For companies sourcing optical modules, Optech provides several practical capabilities.

400G and 200G Product Coverage
Supports both fabric and server-facing network layers.

Optical and Cable Solutions
Modules can be paired with suitable fiber and high-speed interconnect products.

Platform Qualification
Projects can be evaluated against actual switches and NICs.

Customization
Coding, labels, serial numbers and project-specific requirements can be supported.

Taiwan Manufacturing
Optech provides Taiwan-based manufacturing and project support.

Sample-to-Volume Support
Customers can qualify samples before moving to production quantities.

Build Your AI Storage Network with Optech

A high-performance AI cluster needs both:

a fast GPU Compute Network

and

a high-throughput Storage Network.

The reference architecture shown separates storage traffic from compute traffic and uses:

400G Switch-to-Switch optics

200G NIC-to-Leaf optics

RDMA-based networking

and

dedicated fiber connectivity

to provide stable access to distributed datasets.

For companies sourcing the physical layer of these networks, Optech can support:

400G optical transceivers

200G optical transceivers

MTP/MPO connectivity

DAC / AOC

platform qualification

engineering samples

and

volume production.

Contact Optech for 400G / 200G optical module recommendations, samples, switch/NIC compatibility evaluation, project pricing and volume orders.

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FAQ

1. What optical speeds are used in the reference Storage Network?

The reference architecture uses 400G optical transceivers for Switch-to-Switch links and 200G optical transceivers for NIC-to-Leaf links.

2. What type of 400G optical module is shown?

The image shows a 400G QSFP-DD SR8-class transceiver for Switch-to-Switch connectivity.

3. What type of 200G module is shown?

The reference architecture shows a 200G QSFP SR4-class transceiver for NIC-to-Leaf connectivity.

4. What NICs are shown in the architecture?

The image lists ConnectX-6 / ConnectX-7 as NIC options.

5. Why use a separate Storage Network?

Separating storage traffic from GPU compute traffic can reduce network contention and help provide more predictable access to training datasets.

6. What role does RDMA play?

The reference architecture uses an RDMA-based design to improve data exchange efficiency between storage systems and GPU servers while reducing CPU overhead.

7. Does the optical transceiver itself provide RDMA?

No. The transceiver provides the physical Ethernet connection. RDMA is implemented by the NIC, host and network architecture.

8. Why is 400G used between switches while 200G is used at the server?

The switch fabric aggregates traffic from many server links, so higher-capacity 400G uplinks can help avoid network bottlenecks while 200G provides high server-side storage bandwidth.

9. Can Optech provide 400G optical modules for Storage Networks?

Yes. Optech can support 400G optical transceiver requirements for AI, HPC, cloud and storage networking projects according to the target platform and link distance.

10. Can Optech provide 200G NIC-to-Leaf optics?

Yes. Optech can support 200G optical connectivity and related high-speed cable solutions according to the NIC, switch and application.

11. Can Optech support MTP/MPO fiber connectivity?

Optech can work with customers on the required optical interconnect architecture, including parallel fiber and related patch-cord or breakout requirements.

12. Can customers test samples before placing volume orders?

Yes. Customers can begin with engineering samples and platform qualification before moving to volume production.

13. What information should customers provide?

Providing the switch models, NIC model, firmware version, reach, fiber type, connector type, sample quantity and forecast quantity will help Optech evaluate the correct product configuration.

Conclusion

Storage performance is becoming a critical part of AI infrastructure.

A large GPU cluster may have enormous compute capacity, but training efficiency can still suffer if storage data cannot reach the GPUs consistently.

The reference architecture addresses this challenge with a dedicated RDMA-based Storage Network using 400G Spine-Leaf connectivity and 200G NIC-to-Leaf optical links.

For companies sourcing optical modules for these environments, reliable 400G and 200G physical connectivity is essential.

With 400G and 200G optical transceivers, fiber connectivity, DAC, AOC, platform qualification, customization and Taiwan manufacturing, Optech can support the physical network layer required for high-throughput AI storage.

Optech welcomes inquiries from AI infrastructure companies, GPU cloud providers, storage vendors, system integrators, distributors and data center operators looking for reliable 400G / 200G optical solutions.