The shift from conventional cloud computing to large-scale AI training has altered almost every dimension of data centre network design. Where traditional workloads generated predictable, north-south traffic flows between users and servers, AI training clusters create a fundamentally different pattern: dense, synchronised, east-west communication between thousands of GPU accelerators that must collectively function as a single logical compute engine. This traffic pattern — often called all-to-all communication — demands bandwidth, latency, and reliability characteristics that push optical interconnect technology to its limits.

The response from the industry has been a rapid transition through optical port speeds. The 400G generation, which was considered advanced three years ago, is now the baseline access tier for AI data centres. The 800G generation — based on eight lanes of 100 Gb/s each — has become the standard for AI fabric buildouts in 2025, with deployments doubling year-over-year. The 1.6T generation, leveraging 200 Gb/s per-lane technology, is entering early volume production and is already specified in hyperscale procurement contracts for the next infrastructure cycle.

This article covers the engineering rationale behind these transitions — the traffic patterns that demand them, the optical and electrical technologies that enable them, the latency constraints that shape them, and the power and density challenges that complicate them. It examines where pluggable optics remain the practical choice, where Linear Pluggable Optics (LPO) and its asymmetric variant Linear Receive Optics (LRO) offer compelling improvements, and where Co-Packaged Optics (CPO) become the preferred path. It also covers emerging fibre technologies — hollow-core and multi-core fibres — and optical circuit switching, all of which are shaping the next phase of AI cluster design.

2.1 The All-to-All Communication Pattern

Training a large-scale AI model requires distributing computation across hundreds or thousands of GPU accelerators simultaneously. The work is split in several ways — across training data (data parallelism), across model layers (pipeline parallelism), and across the weights within a layer (tensor parallelism). Each parallelism strategy produces a specific communication pattern, and in combination they create demand for high-bandwidth, low-latency collective communication operations such as AllReduce, AllGather, and ReduceScatter.

An AllReduce operation requires every accelerator in a training group to share its locally computed gradient updates with all other accelerators in the group, aggregate the results, and distribute the aggregated result back to every accelerator — all before the next training step begins. In a cluster of thousands of GPUs, this generates a simultaneous, bidirectional, many-to-many traffic burst that is wholly unlike web traffic, database queries, or file I/O. It is a synchronised collective operation, meaning the fastest accelerator can only advance as fast as the slowest interconnect allows.

2.2 The Scale-Up and Scale-Out Network Layers

Modern AI cluster networks are organised into two physically distinct layers. The scale-up network connects GPU accelerators within a single server or a tightly coupled node group at the highest possible bandwidth and lowest latency. This layer uses specialised chip-to-chip interconnects, PCIe, or CXL switches, and is internal to the compute node — transparent to the Ethernet fabric.

The scale-out network (also called the backend AI fabric) connects compute nodes across the cluster. This is where 800G and 1.6T optical transceivers are deployed. It must carry collective communication traffic with near-zero congestion, provide a non-blocking topology, and deliver end-to-end latency that keeps collective operations within the compute step window.