Standard batching techniques, effective for dense models, encounter significant hurdles when applied directly to Mixture of Experts (MoE) models during inference. The core challenge stems from the conditional computation inherent in MoEs: different tokens within the same input batch are routed to different experts based on the gating network's decisions. This dynamic, token-level routing disrupts the computational uniformity that makes traditional batching efficient.

The Challenge of Sparse Activation in Batches

Consider a standard transformer inference scenario. A batch of input sequences is processed layer by layer. Within each layer, all tokens in the batch undergo the same computations (e.g., self-attention, feed-forward network). This homogeneity allows for efficient parallel processing on hardware like GPUs, maximizing throughput.

In an MoE layer, however, the path diverges after the gating network. For a batch containing $B$ sequences of length $L$ , the $B \times L$ tokens pass through the gating network. Each token is then assigned to one or more experts (typically top-k, often k=1 or k=2 at inference). If we have $N$ experts, the tokens originally grouped by sequence position are now logically scattered across these $N$ computational paths.

A naive batching approach, simply feeding the input batch to the MoE layer, leads to several inefficiencies:

Underutilized Experts: Some experts might receive very few tokens (or even zero) from a given batch, while others might be overloaded. This leads to poor hardware utilization, as processing cores dedicated to idle experts sit unused.
Load Imbalance: Even if experts are not completely idle, the number of tokens processed by each expert can vary significantly. This imbalance means the total time for the MoE layer is dictated by the most heavily loaded expert, negating potential speedups.
Increased Latency: Waiting for the slowest expert path dominates the layer's execution time.

These issues are particularly pronounced when experts are distributed across multiple devices (Expert Parallelism). Naively processing the batch would require inefficient, sparse communication patterns or result in severe load imbalance across devices.

Strategies for Efficient MoE Inference Batching

To overcome these challenges, inference batching for MoEs requires strategies that explicitly handle the dynamic routing of tokens. The primary goal is to regroup tokens after the gating decision but before expert computation, ensuring that each expert processes a dense, reasonably sized batch of tokens assigned to it.

Dynamic Batching (Request-Level)

Dynamic batching is a general technique used in serving systems where incoming inference requests are buffered and grouped together to form larger batches before being processed by the model. While beneficial for overall system throughput by increasing hardware utilization, it doesn't inherently solve the MoE-specific problem of intra-batch routing divergence. It increases the total number of tokens processed together, which can statistically improve expert load balance compared to single-request processing, but it doesn't guarantee uniform load distribution within the dynamically formed batch. It's often used in conjunction with more MoE-specific techniques.

Token-Level Grouping and Permutation

This is the cornerstone strategy for efficient MoE inference. It involves actively rearranging tokens within a batch based on their assigned expert. The workflow typically looks like this:

Gating Computation: The gating network processes all tokens in the incoming batch (potentially formed via dynamic batching) to determine expert assignments for each token.
Routing Decision & Sorting: Identify the target expert(s) for each token. Often, only the top-1 expert is used at inference for simplicity and speed, but top-2 routing is also possible. Tokens are then sorted or indexed based on their assigned expert ID.
Token Permutation (Gather): Tokens are physically rearranged in memory (or logically addressed) such that all tokens destined for Expert 1 are contiguous, followed by all tokens for Expert 2, and so on. If experts are distributed across devices, this step involves an All-to-All communication pattern, similar to training, where each device sends the tokens destined for remote experts and receives tokens assigned to its local experts. Efficient implementations often use optimized collective communication libraries (e.g., NCCL for NVIDIA GPUs) or specialized frameworks like Tutel.
Batched Expert Computation: Each expert (or device hosting experts) now receives a dense mini-batch of tokens specifically assigned to it. It performs its computation (e.g., the feed-forward network) efficiently on this grouped set of tokens.
Token Un-permutation (Scatter): The processed token representations must be rearranged back into their original sequence order within the batch. This mirrors the permutation step, potentially involving another All-to-All communication if distributed.
Combine & Continue: The outputs from the experts are combined (often a weighted sum based on router logits, even if only top-1 was used for computation), and the resulting batch proceeds to the next layer in the model.

The following diagram illustrates the conceptual flow of token permutation for MoE inference:

Flow of token processing within an MoE layer during inference using token-level grouping and permutation. Tokens are routed, grouped by expert, processed, and then reassembled.

Managing Expert Capacity

During training, an expert_capacity is typically defined, often with a capacity_factor > 1.0, to handle temporary imbalances and allow some slack. At inference, this capacity still plays a role. If the number of tokens assigned to a specific expert within a batch exceeds its defined capacity (Number of Tokens / Number of Experts * capacity_factor), tokens might be dropped.

While dropping tokens is sometimes tolerated in training (and managed via auxiliary losses), it's generally undesirable at inference as it leads to information loss and degraded output quality. Strategies to handle potential overflows at inference include:

Sufficient Capacity: Ensure the capacity configured for inference is large enough to handle expected peak loads for typical batch sizes. This might involve profiling typical routing patterns.
Padding: If an expert receives fewer tokens than its capacity, its batch can be padded to maintain computational uniformity. This is standard practice in many MoE implementations.
No Dropping: Configure the system to avoid dropping tokens, potentially by increasing capacity dynamically or accepting higher latency if an expert is momentarily overloaded (though this complicates implementation). Using Top-1 routing simplifies capacity management compared to Top-2.

Trade-offs and Considerations

Token-level grouping significantly improves throughput by maximizing expert utilization and leveraging hardware parallelism effectively. However, it introduces overhead:

Latency: The sorting, permutation, and un-permutation steps (especially the All-to-All communication in distributed settings) add latency to each MoE layer compared to a dense equivalent. This is a direct trade-off against throughput.
Implementation Complexity: Requires careful management of token indices, efficient permutation kernels, and potentially integration with specialized libraries (e.g., Tutel, DeepSpeed).
Memory: Buffers are needed to store tokens during the permutation and un-permutation stages.

Choosing the right batching strategy involves balancing these factors based on the specific application's requirements (e.g., latency-sensitive real-time inference vs. throughput-oriented batch processing) and the deployment environment (single GPU, multi-GPU node, multi-node cluster). Effective batching is not just an optimization; it is fundamental to achieving practical inference performance with large-scale MoE models.