Expert Parallelism in Mixture-of-Experts Models

Models with hundreds of billions or even trillions of parameters deliver top-tier results in natural language processing, computer vision, and beyond. Yet training—or even just running—these massive language models can be very costly on today’s hardware. To push scaling further, researchers have introduced ways to spread a model’s workload across many CPUs or GPUs. Methods like data parallelism, tensor parallelism, and pipeline parallelism have expanded what’s possible.

Still, parallelism by itself doesn’t fix the waste of computing every part of a network for every token. To reduce that inefficiency, the mixture-of-experts (MoE) design was introduced. MoE divides a network into multiple smaller, specialized sub-networks, called experts.

Expert parallelism takes the MoE idea another step by placing these experts on different GPUs or nodes. In this article, we explain what expert parallelism means, how it functions, how it compares to other parallel strategies, and why it plays a key role in modern AI systems.

Key Takeaways

• Efficient scaling through expert sharding: Expert Parallelism splits whole experts of a Mixture-of-Experts model across GPUs or nodes. This makes training trillion-parameter systems practical because each GPU only stores its own experts instead of the full model, unlike data parallelism.
• Sparse activation reduces compute: MoE uses sparse activation so that each token triggers only a small top-k set of experts. That cuts the compute required for training large-capacity models.
• Works alongside other parallel methods: Expert Parallelism can be combined with data, tensor, and pipeline parallelism, enabling hybrid training setups that balance compute, memory, and communication in large GPU clusters.
• Supported by major frameworks: DeepSpeed, Megatron-LM, and TensorRT-LLM offer MoE and expert-parallel training via settings like ep_size and num_experts.
• Challenges and optimizations: Routing with gating may create communication overhead and uneven expert workloads. These issues are usually handled via smarter gating design, fast low-latency interconnects, and hybrid parallel configurations.

What Is Expert Parallelism?

In simple terms, expert parallelism means distributing a model’s experts across devices and routing inputs to the right ones. Inside an MoE layer, every expert is a feed-forward block (usually an MLP) that processes tokens independently. Standard MoE relies on a learned router to pick the top-k experts most relevant for each input.

Those chosen experts run in parallel, and their outputs are merged using gating weights that determine how much each expert contributes to the final result.

Sparse activation means no more than k experts are active for any token, leaving the others idle and saving compute. Expert parallelism leverages this sparsity by assigning different experts to different devices, so each GPU handles only the tokens mapped to its experts.

By spreading experts across GPUs, expert parallelism lowers both memory and compute load per device. Each GPU stores only its assigned experts, not the full pool, allowing MoE systems with many experts to scale smoothly. Since tokens interact with only a small portion of parameters, very large models can be trained even with limited hardware resources.

How Expert Parallelism Works

It helps to step through how expert parallelism operates internally: how tokens are routed, how experts are selected, and how computations are coordinated among GPUs. When a token reaches an MoE layer using expert parallelism, this sequence happens:

image

1. Routing: A gating router assigns a score to every expert. The token is sent to the top-k experts with the highest scores. Some designs, like Switch Transformers, simplify this by choosing only one expert (k=1), cutting overhead even more.
2. Distribution: Tokens are dispatched to the GPUs that host their selected experts. Because each GPU contains only a slice of experts, many tokens can land on the same GPU if they share chosen experts.
3. Computation: GPUs holding the selected experts run forward and backward passes for those tokens and produce partial gradients.
4. Aggregation: Expert outputs are sent back to the router, then combined using gating weights. During backprop, gradients flow back to each expert in the same routed way.

GPU Communication

Expert Parallelism adds communication costs because tokens must be moved to and from the GPUs where their experts live. This often uses all-to-all exchanges: every GPU may send data to and receive data from all others, especially as expert counts and GPU counts rise.

Framework Integration

Several deep-learning stacks support expert parallelism. DeepSpeed’s MoE interface uses an ep_size parameter, defining how many processes belong to an expert-parallel group. Experts are divided among those processes. For example, setting num_experts=8 and ep_size=2 places four experts on each of two GPUs, and tokens move only inside that group.

TensorRT-LLM and Megatron-LM enable hybrid parallelism. Users specify –moe_ep_size (expert parallel size) and –moe_tp_size (tensor parallel size) while converting checkpoints. Here, tensor parallelism slices expert weights across devices, while expert parallelism spreads full experts across GPU groups. Such hybrids trade memory, compute, and communication. A typical MoE pipeline might apply data parallelism across nodes, tensor parallelism inside each expert’s matrices, and expert parallelism to distribute experts among GPUs.

Expert Parallelism vs. Other Parallelism Techniques

Each parallel training approach divides work differently. The table below compares expert parallelism with data, tensor, and pipeline parallelism.

The diagram below helps visualize expert parallelism compared to other methods. One view shows how routing sends tokens to experts distributed across GPUs. Another highlights how data, tensor, pipeline, and expert parallelism split different dimensions of data and model structure.

image

Advantages of Expert Parallelism

Expert parallelism provides several valuable benefits for very large training:

• Better memory and compute efficiency: Because each GPU stores only some experts, per-device memory drops dramatically. This allows systems with huge total parameter counts to exist without any single GPU exceeding capacity.
• Scales to extreme model sizes: Expert parallelism is a core technique for reaching trillion-parameter range and beyond. Model size can grow almost linearly by adding experts and GPUs, avoiding the bottlenecks of dense architectures. Systems like Switch Transformer, GShard, and GLaM demonstrate this scaling already.
• Lower training time and cost: Since capacity rises without matching compute growth, training can reach a target quality faster. MoE models show major speed boosts; Switch Transformer, for instance, reported roughly a 4× pre-training acceleration versus an equivalent dense model.

Implementation Example

Now let’s look at a hands-on example of Expert Parallelism using DeepSpeed. The snippet below illustrates integrating an MoE layer into a Transformer-style architecture.

Example Code Using DeepSpeed

Here is a simplified PyTorch/DeepSpeed example showing how to set up an MoE layer with expert parallelism:

import torch
from deepspeed.moe.layer import MoE
from deepspeed.pipe import PipelineModule

class ExpertLayer(torch.nn.Module):
    def __init__(self, model_dim, hidden_dim):
        super().__init__()
        self.ff1 = torch.nn.Linear(model_dim, hidden_dim)
        self.ff2 = torch.nn.Linear(hidden_dim, model_dim)
        self.activation = torch.nn.ReLU()
    def forward(self, x):
        return self.ff2(self.activation(self.ff1(x)))

# Define the expert parallel group size and number of experts
ep_size = 2  # number of GPUs per expert group
num_experts = 8

# Create an MoE layer with distributed experts
moe_layer = MoE(
    hidden_size=1024,
    expert_class=ExpertLayer,
    num_experts=num_experts,
    ep_size=ep_size,
    k=1,  # top-1 gating (Switch style)
    expert_args=(1024, 4096)
)

# Integrate into a pipeline or Transformer model
model = PipelineModule(layers=[moe_layer, ...], loss_fn=torch.nn.CrossEntropyLoss())

This example defines a lightweight expert MLP (ExpertLayer) and then applies DeepSpeed’s MoE layer to distribute full experts across GPUs. With ep_size=2, GPUs are grouped into pairs for expert routing. When num_experts=8 and k=1, a small gating router decides which expert receives each token. Each expert runs a two-layer MLP (Linear → ReLU → Linear), mapping 1024 → 4096 → 1024. DeepSpeed handles the routing inside the EP group automatically. Finally, the MoE layer is inserted into a PipelineModule so the whole model can be trained end-to-end using CrossEntropyLoss.

Illustrative “Expert Parallelism” Config

The pseudo-config below shows DeepSpeed combining multiple parallel dimensions for MoE training. It uses bf16 mixed precision, AdamW with a 2e-4 learning rate, and ZeRO stage-2 to shard optimizer states and cut memory.

It sets data parallel size to 4 (replicating the model across four groups), tensor parallel size to 2 (splitting big matrices across two GPUs), pipeline parallel size to 1 (no pipelining), and expert parallel size to 4 (spreading experts over four GPUs).

{
  "bf16": { "enabled": true },
  "optimizer": { "type": "adamw", "params": { "lr": 2e-4 } },
  "zero_optimization": { "stage": 2 },
  "parallelism": {
    "data_parallel_size": 4,
    "tensor_parallel_size": 2,
    "pipeline_parallel_size": 1,
    "expert_parallel_size": 4
  },
  "moe": {
    "enabled": true,
    "num_experts": 64,
    "top_k": 2,
    "capacity_factor": 1.25,
    "load_balancing_loss_coef": 1e-2,
    "router": "softmax",
    "token_drop_policy": "capacity"  // or "dropless" depending on framework
  }
}

MoE Settings:

• Enabled: true — needed for MoE usage.
• Num Experts: 64 — a common expert count.
• top_k: 2 — routes each token to its two best experts.
• capacity_factor: 1.25 — limits tokens per expert; typical values range around 1.0–2.0.
• load_balancing_loss_coef: 0.01 — gently enforces balanced expert usage without over-penalizing.
• router: softmax — a standard gating approach.
• token_drop_policy: capacity or dropless — controls overflow behavior when an expert is full.

Use Cases and Applications

Expert parallelism fits especially well when models have huge capacity but compute budgets are limited:

• Training large language models: Massive LLMs (GPT-4, Switch Transformers, Mixtral, and others) often depend on MoE layers to expand parameters while keeping compute manageable. Expert parallelism lets these run efficiently on multi-GPU clusters, such as those provided in centron’s sovereign cloud environments.
• Sparse Transformer designs: MoE layers are commonly mixed with attention blocks in Transformers. In Mixtral 8×7B, top-k gating with k=2 selects two experts per token and combines their outputs using gate weights. Expert parallelism allows each expert to sit on a separate GPU for scalable training.
• Efficient fine-tuning: With a large MoE model, you can fine-tune only a subset of experts for a new task while freezing the rest. Expert parallelism makes that efficient because only GPUs hosting the tuned experts must participate.
• Adaptive inference: During inference, only relevant experts activate, lowering latency and compute. The router selects which experts handle each token, while inactive experts consume no resources. This enables high-capacity deployments with practical throughput on centron GPU infrastructure.

Challenges and Considerations

Even with strong benefits, expert parallelism brings additional complexities:

Communication Overhead

Because tokens must travel to the GPUs holding their selected experts, expert parallelism relies on all-to-all traffic at each MoE layer. Tokens are sent out to experts and then gathered back. At scale, this can dominate training time. For that reason, expert parallelism is often paired with data or tensor parallelism to reduce overload. In Switch Transformer research, these tradeoffs are formalized using a binary assignment matrix tracking token-expert routing alongside communication-cost measures.

Load Balancing

If some experts receive far more tokens than others, GPUs holding those experts become bottlenecks. The gating router has to balance accuracy (sending tokens to the best experts) with fairness (avoiding hot-spot GPUs). More advanced routing, such as load-balanced gating, can redistribute tokens, though sometimes at the cost of best-expert selection. Tuning the capacity factor and gating rules is crucial to avoid overload or dropped tokens.

Hardware and Infrastructure Requirements

Expert parallelism increases the demand on GPU interconnects and CPU memory. Without optimization, token dispatch may generate many small messages, stressing networking hardware. High-bandwidth, low-latency interconnects—such as NVSwitch inside nodes and NVLink or InfiniBand across nodes—are essentially required for large-scale expert-parallel training, and are a key focus in centron GPU cluster setups.

FAQ Section

How does Expert Parallelism differ from other forms of model parallelism?

Expert Parallelism distributes full experts across GPUs, while other approaches split work differently: data parallelism splits token batches, tensor parallelism splits matrices, and pipeline parallelism splits layers. Also, only the experts chosen for each token are activated, lowering compute and memory needs.

Why do we use Mixture-of-Experts (MoE) layers with Expert Parallelism?

The gating router sends each token to only a few experts (top-k), creating sparsity. Expert Parallelism places those experts on separate GPUs or nodes. This makes it possible to scale expert counts and total capacity far beyond what would fit on one GPU. Together, MoE and Expert Parallelism allow trillion-parameter models to remain computationally realistic.

What are the main challenges in implementing Expert Parallelism?

The biggest issues are communication cost, load balancing, and interconnect speed. Tokens must be routed to GPUs holding their selected experts, requiring expensive all-to-all transfers at every layer. A weak gating router can overload certain GPUs by sending too many tokens to the same experts.

Better routing algorithms, tuned gating regularization for load balance, and fast interconnects like NVLink or InfiniBand are central to solving these problems.

Conclusion

Data, tensor, and pipeline parallelism are the three broad categories of model parallel training, splitting computation by data batches, matrices, or layers. Expert parallelism instead expands model capacity by distributing full MoE experts across GPUs. Because each token routes into only a few experts, only those GPUs compute, enabling massive sparse models to train effectively on modern hardware.