Diffusion model inference, particularly the iterative denoising process, heavily utilizes GPU resources. A single inference request, even for a moderately sized image, might not fully saturate the computational capacity of modern GPUs. Processing requests one by one can lead to significant underutilization, where the GPU spends much of its time idle between requests or performing memory transfers for small amounts of data. Request batching directly addresses this inefficiency.

The fundamental idea is simple: instead of sending single inference requests to the model worker immediately upon arrival, the system groups multiple requests together into a "batch". This batch is then processed simultaneously by the diffusion model on the GPU. Because operations like matrix multiplications within the neural network can often be performed more efficiently on larger tensors (representing the batch), processing a batch of $N$ requests typically takes significantly less than $N$ times the duration of processing a single request.

Dynamic Batching Implementation

For real-time inference APIs, dynamic batching is the most common and effective strategy. Unlike static batching where the batch size is fixed, dynamic batching adapts to the incoming request load. Here's how it typically works:

Buffering: Incoming inference requests are not sent directly to the model worker. Instead, they are held temporarily in a short-term buffer or queue.
Batch Formation: The system waits for a short period (a configurable timeout) or until a certain number of requests (a maximum batch size) have accumulated in the buffer.
Batch Processing: Once the timeout expires or the maximum batch size is reached, whichever happens first, the collected requests are formed into a single batch tensor. This batch tensor is then sent to the model inference engine (running on the GPU) for processing.
Result Splitting: After the model generates outputs for the entire batch, the system unpacks the results and maps them back to their original individual requests.
Response Delivery: Finally, the individual results are sent back to the respective clients.

Diagram illustrating the flow of requests through a dynamic batching system. Requests are buffered, grouped, processed together on the GPU, and results are then split.

Trade-offs: Throughput vs. Latency

The primary benefit of batching is significantly increased throughput. By keeping the GPU busier and leveraging parallel processing capabilities more effectively, the system can handle a higher volume of requests per unit of time.

However, this comes at the cost of potentially increased latency for individual requests. A request might have to wait in the buffer for the batch to fill up or for the timeout to expire, even if the GPU is currently idle. This added waiting time increases the end-to-end latency compared to processing the request immediately.

Performance characteristics often observed when implementing batching. Throughput generally increases with batch size (up to a point), while average latency also tends to increase due to waiting times in the buffer. Tuning involves finding an optimal balance.

Implementation Considerations

Timeout Tuning: The batching timeout is a critical parameter. A short timeout minimizes the added latency but may result in smaller, less efficient batches during periods of low traffic. A long timeout maximizes batch size and throughput but can lead to unacceptable latency for users. Optimal values often depend on the specific model, hardware, and expected load patterns, requiring empirical tuning.
Maximum Batch Size: This limit is typically dictated by the available GPU memory. Larger batches consume more memory. Setting this too high can lead to out-of-memory (OOM) errors.
Handling Heterogeneous Requests: Requests within a batch might have different parameters (e.g., prompts, negative prompts, guidance scale, number of inference steps, random seeds). The inference server and model code must be designed to handle this. Prompts can often be batched directly (requiring padding if using transformers). Different step counts or samplers within the same batch might be more complex or impossible, potentially requiring requests with incompatible parameters to be processed in separate batches or sequentially.
Framework Support: Many serving frameworks (like NVIDIA Triton Inference Server, TorchServe, TensorFlow Serving) provide built-in support for dynamic batching, handling the buffering, timeout logic, and batch formation automatically. Using these frameworks can significantly simplify implementation.
Un-batching Logic: Ensure the mechanism for splitting the batched results and routing them back to the correct original request is reliable. This is usually handled by the batching middleware or framework.

Request batching is a standard and highly effective technique for improving the throughput and cost-efficiency of GPU-based inference, especially for compute-intensive tasks like diffusion model image generation. Careful tuning of batching parameters is necessary to balance the gains in throughput against the potential increase in request latency.