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Yes—but mainly in the networking portion of an AI data center, and only when the optical design fits the topology and traffic. Optical circuit switches (OCS) can keep data optical through a connection, removing some electrical switching stages and optical-electrical-optical conversions. A 2025 peer-reviewed AI-data-center study modeled 12% lower network power than a comparable Leaf-Spine Ethernet architecture. That is an architecture-specific result, not a guaranteed reduction in a facility’s total electricity bill.
The practical answer depends on what is being measured: OCS, co-packaged optics (CPO), linear-drive optics (LPO) and digital-signal-processor (DSP) transceivers address related but different parts of the network. GPU, memory, storage, power-delivery and cooling loads can still dominate the site’s energy use.
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What an optical switch changes in an AI network
Conventional AI-cluster fabrics move traffic through electrical switch ASICs and electrical links. At various points, optical signals from a fiber are converted to electrical signals, processed or switched, and converted back to light. Each conversion requires active electronics, and every switch stage adds its own power and heat.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →An optical circuit switch establishes an optical path between ports. Once the circuit is configured, the payload can pass through the switching fabric without being repeatedly converted to electricity and back to light. The control electronics still consume power, and the endpoints still require optical and electrical interfaces, but the data path can avoid some high-speed packet-processing stages.
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This is why an OCS can reduce network power rather than GPU power. It does not make a GPU execute a training step more efficiently; it can reduce the energy needed to move gradients, parameters and other data between GPUs. The benefit is largest when the optical path replaces substantial electrical switching or OEO processing.
OCS is also a scheduling and topology technology. Circuits must be established, changed or reserved for traffic patterns. A design therefore trades some packet-switching flexibility for lower data-path overhead and potentially lower latency. Open Compute Project material positions optical switching for the high-bandwidth, low-latency and energy-efficiency requirements of AI, machine learning and high-performance-computing clusters.
What published studies actually measured
The strongest directly AI-specific power figure in the available evidence comes from a 2025 peer-reviewed study of a PCIe-over-optical architecture. Its modeled system used 12% less power than a comparable Leaf-Spine Ethernet solution. The same modeled comparison reported lower cost, latency and training completion time, but those results belong to the study’s architectures and workloads rather than to every OCS deployment.
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| Result | Value | Scope and qualification |
|---|---|---|
| Power consumption | 12% lower | 2025 AI-data-center model comparing PCIe-over-optical with a Leaf-Spine Ethernet architecture |
| Modeled cost | 31.6% lower | Same 2025 modeled architecture comparison |
| Latency | 3 microseconds over 62 m of fiber | Reported by the 2025 PCIe-over-optical study |
| Backpropagation completion time | 48.3% lower | Reported for the study’s workload compared with PCIe with Ethernet |
| CPO energy efficiency versus LPO | 23% better | 2026 Journal of Lightwave Technology component-level comparison |
| CPO energy efficiency versus DSP | 67% better | 2026 Journal of Lightwave Technology component-level comparison |
| CPO energy efficiency versus LPO, including host SERDES | 44% better | 2026 modeled comparison including host serializer/deserializer power |
| CPO energy efficiency versus DSP, including host SERDES | 69% better | 2026 modeled comparison including host serializer/deserializer power |
| Modeled 102.4-Tbps switch with CPO | 3,910 W | 2026 Journal of Lightwave Technology model |
| Modeled 102.4-Tbps switch with LPO | 4,648 W | 2026 Journal of Lightwave Technology model |
| Modeled 102.4-Tbps switch with DSP | 5,714 W | 2026 Journal of Lightwave Technology model |
The CPO figures are important context but are not an OCS-only result. They compare optical transceiver and switch-link implementations. A reader should not combine the CPO wattage difference with the AI study’s network-level percentage or treat either as a universal facility saving.
Why topology and traffic can erase the benefit
Optical switching is not automatically efficient in every network shape. DTU simulations found power reductions in the evaluated Fat-Tree and ring architectures, while a traditional Tree topology did not benefit. In that Tree case, inefficient traffic grooming prevented the optical design from using its circuits effectively.
Traffic grooming means grouping flows so that a configured optical circuit carries useful traffic for enough time. If flows arrive unpredictably, are too small, or require frequent circuit changes, the control overhead and underused capacity can offset the lower data-path power. AI training often has structured communication phases, which can help, but the pattern depends on the collective operation, job mix and scheduling policy.
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Topology questions to answer
- Is traffic concentrated in predictable GPU-to-GPU or rack-to-rack patterns?
- Can the fabric schedule circuits for those patterns without excessive reconfiguration?
- Does the proposed topology avoid stranded optical capacity when communication shifts?
- Are failure paths available when an optical element, link or control component is unavailable?
A topology that looks efficient on a diagram can perform poorly if its circuits sit idle or if packets must fall back through additional electrical stages. Simulation should therefore use representative job traces, not only uniform traffic.
OCS, CPO, LPO and DSP are different technologies
These terms are often mixed in power-saving claims, but they describe different layers of a network.
| Term | What it describes | How it relates to power |
|---|---|---|
| OCS | An optical circuit-switching fabric that creates optical paths between ports | Can remove electrical packet-switch stages and some OEO conversions when traffic is suitable for circuits |
| CPO | Co-packaged optics placed close to or with a switch ASIC | Shorter electrical reach can reduce interface energy; the 2026 model reported 3,910 W for a 102.4-Tbps switch |
| LPO | Linear-drive optical transceiver approach with less digital processing than DSP links | The 2026 model reported 4,648 W for the same modeled 102.4-Tbps switch |
| DSP | Optical links that use digital signal processors for signal conditioning | The 2026 model reported 5,714 W for the same modeled 102.4-Tbps switch |
An AI cluster can use more than one of these technologies. For example, an OCS may provide reconfigurable optical paths while CPO, LPO or DSP determines the energy of the transceivers and switch links at the endpoints. Any comparison should state which layer is being changed.
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How much electricity could an AI data center save?
There is no single percentage that can be applied to every facility. The AI-specific modeled result establishes a possible network-level reduction under defined assumptions. The fraction of total site power saved will be smaller when GPUs, memory, storage, power supplies and cooling are included in the denominator, unless networking is an unusually large share of the facility load.
To convert a network estimate into a defensible facility estimate, define the accounting boundary before calculating:
- Switch ASICs and packet-processing electronics
- Optical circuit-switch control electronics and actuators
- Optical transceivers, lasers and receivers
- Host SERDES and retimers
- Electrical and optical cables
- Power supplies and conversion losses
- Cooling attributable to the networking equipment
- GPU, CPU, memory and storage power, if claiming a whole-facility result
Measure or model the baseline and optical design at the same delivered bandwidth, port count, utilization, redundancy level and workload. A lower transceiver figure is not comparable with a facility power-use effectiveness figure, and a modeled switch result is not a field measurement of an operating data center.
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When OCS is a strong candidate
OCS deserves serious evaluation when the network has all or most of these characteristics:
- Large GPU fabrics where network power and heat are material operating costs
- High, recurring bandwidth between known groups of GPUs or racks
- Communication phases predictable enough for circuit scheduling
- A topology that simulations show can groom traffic efficiently
- Enough scale to justify specialized optical hardware and control software
- A requirement for topology reconfiguration or resilient alternate paths
Optica has identified OCS uses in failure resilience and reconfigurable topologies for deep-learning workloads. Those capabilities can matter independently of energy: an optical fabric may let operators redirect connections around a failed component or adapt the topology to a changing job placement.
When conventional packet switching may remain preferable
A packet-switched electrical fabric can be the safer choice when traffic is highly irregular, workloads are small or short-lived, or broad interoperability is more important than peak efficiency. Frequent circuit setup and teardown can add operational complexity. A design that needs unrestricted any-to-any connectivity may also require packet switching as a fallback, reducing the fraction of traffic that uses the low-power optical path.
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Cost must be evaluated at system level. The 2025 study modeled lower cost for its PCIe-over-optical design, but that does not establish a universal procurement advantage. Optics, control systems, integration, spares, training and software can change the business case. The relevant comparison is the cost of delivering a required amount of usable GPU bandwidth and availability, not the price of one switch.
A practical evaluation procedure
- Set the boundary. Write down whether the target is link power, switch power, networking power, cooling power or whole-facility electricity.
- Record the baseline. Capture switch and transceiver models, port speeds, utilization, cable lengths, redundancy and measured input power for the existing fabric.
- Trace real workloads. Collect GPU collective-communication traces across training, inference and checkpointing jobs. Include quiet periods and job mixes.
- Model candidate topologies. Compare the proposed OCS design with the existing Leaf-Spine, Fat-Tree, ring or Tree arrangement at the same bandwidth and resilience target.
- Test grooming and reconfiguration. Quantify circuit utilization, setup frequency, blocked flows and fallback traffic. Do not assume that nominal link capacity becomes delivered capacity.
- Separate component claims. Report OCS-fabric power separately from CPO, LPO or DSP transceiver power, host SERDES, switch ASICs and cooling.
- Validate failure behavior. Measure recovery paths and degraded-mode power when a link, optical element or controller fails.
- Compare total cost and operations. Include control software, integration, maintenance, spares and staff effort alongside capital equipment.
- Pilot under representative load. Use the same job traces and instrumentation for electrical and optical configurations before extrapolating to a full cluster.
Common mistakes in interpreting optical-switch claims
- Turning a modeled percentage into a facility guarantee. A network model does not include every electrical and cooling load unless it says so.
- Mixing technology layers. CPO efficiency numbers cannot be presented as proof that an OCS fabric alone saves the same amount.
- Ignoring topology. DTU results show that a topology with poor traffic grooming may see no reduction.
- Comparing unequal capacity. A lower-power design that delivers less bandwidth or less redundancy is not an equivalent replacement.
- Counting only peak wattage. Idle, partial-load and reconfiguration behavior can determine annual energy use.
- Omitting host interfaces. Host SERDES changed the relative CPO, LPO and DSP efficiency results in the 2026 analysis.
Bottom line for AI data-center planners
Optical circuit switches can reduce AI-network power when they replace electrical switching and OEO conversions on traffic patterns that can be groomed into efficient circuits. The evidence supports a meaningful opportunity, including the 2025 modeled AI-network reduction, but it does not support a universal whole-data-center percentage.
The credible way to make a decision is to model and measure the same boundary on both designs, with real GPU traffic, equal bandwidth and equal resilience. Treat OCS as a system architecture choice, and keep its results separate from transceiver choices such as CPO, LPO and DSP. If the topology cannot keep optical circuits busy, the expected power benefit may disappear.
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