As introduced earlier in this chapter, scaling Mixture of Experts models confronts us with substantial memory demands, primarily driven by the potentially large number of individual expert networks. Standard Data Parallelism (DP), where each worker holds a complete replica of the model, quickly becomes impractical as the number of experts grows into the dozens or hundreds. Each expert might itself be a multi-layer perceptron (MLP) with millions of parameters. Storing all experts on every device leads to prohibitive memory consumption.

Expert Parallelism (EP) directly addresses this challenge by partitioning the experts within an MoE layer across the available computational devices (e.g., GPUs). Instead of each device holding all $N$ experts, each device holds only a fraction, typically $N/D$ , where $D$ is the number of devices participating in the expert parallel group.

The Mechanics of Expert Parallelism

Consider an MoE layer within a transformer block. Under Expert Parallelism, the sequence of operations for processing a batch of tokens involves these steps:

Local Gating: Each device processes its local shard of input tokens through the shared components of the transformer block up to the MoE layer's gating network. The gating network, often being relatively small, is typically replicated on all devices (similar to data parallelism). It computes the expert assignments for its local tokens.
Token Routing (All-to-All Send): This is the defining communication step of EP. Based on the gating decisions, each device determines which of its local tokens need to be processed by experts residing on other devices. An All-to-All communication primitive is used to exchange tokens among devices. Device $i$ sends the tokens destined for experts on device $k$ directly to device $k$ .
Parallel Expert Computation: Once the tokens arrive at the correct devices, each device computes the outputs for the tokens it received using its local subset of experts. Since each device only holds $N/D$ experts, the memory footprint is significantly reduced. This computation happens in parallel across all devices in the EP group.
Result Gathering (All-to-All Receive): After expert computation, the processed token representations must be returned to their original devices to maintain sequence integrity for subsequent layers. Another All-to-All communication step gathers these results, ensuring each device receives the outputs corresponding to the tokens it initially processed in step 1.

The diagram below illustrates this flow across four devices, each holding two distinct experts.

Distribution of 8 experts across 4 devices (2 experts per device). Dashed lines represent the first All-to-All communication (sending tokens $T$ based on gating assignments to target experts $E_i$ ). Dotted lines represent the second All-to-All (returning processed tokens $P(T)$ to their originating device). Only a subset of routes is shown for clarity.

Advantages and Costs

The primary advantage of Expert Parallelism is memory efficiency. By partitioning the experts, you can instantiate MoE models with a vastly larger total number of parameters than would fit onto a single device. This allows for scaling model capacity (through more experts) without proportionally increasing the memory burden on individual workers. It also distributes the computational load of the expert forward and backward passes.

However, this benefit comes at the cost of increased communication. The two All-to-All operations are communication-intensive, especially at large scales. Their latency and bandwidth requirements can become significant bottlenecks, potentially limiting overall training throughput if not carefully managed. Optimizing this communication is a major focus when scaling MoE models, as discussed later in this chapter.

Implementation Considerations

Expert Placement: While simple round-robin assignment of experts to devices is common, more sophisticated strategies might consider network topology to minimize communication costs between frequently communicating devices.
Framework Integration: Implementing efficient All-to-All communication and managing the distributed state requires specialized libraries. Frameworks like DeepSpeed (using its MoE implementation) and Tutel abstract much of this complexity, providing optimized communication kernels and integration with standard deep learning frameworks like PyTorch. These libraries handle the token shuffling and expert computation coordination.
Interaction with Other Parallelism: Expert Parallelism is typically used in conjunction with Data Parallelism. A common setup involves replicating the non-MoE layers using DP, while partitioning the MoE layers' experts using EP across the same set of devices. We will examine this integration in the next section.

In summary, Expert Parallelism is a foundational technique for scaling Mixture of Experts models. It partitions experts across devices, enabling massive model sizes by reducing per-device memory requirements, but introduces significant All-to-All communication overhead that must be carefully considered and optimized.