Dynamic Batching Strategies: Balancing Throughput and Latency in Production LLM Serving

Deploying Large Language Models (LLMs) in production is a classic engineering trade-off challenge. On one hand, you want maximum throughput to serve as many requests as possible, driving down cost-per-token. On the other, you need low latency to ensure a responsive user experience. Static batching—where you simply collect a fixed number of requests before processing—often fails to balance these competing needs effectively. This is where dynamic batching shines.

Dynamic batching intelligently groups requests on-the-fly, adapting to the current load and request characteristics. In this post, we will explore the architecture of dynamic batching, the critical latency thresholds involved, and how to implement effective strategies in modern LLM serving frameworks.

The Core Challenge: The Batching Latency Curve

When serving LLMs, batching improves hardware utilization (especially on GPUs) by keeping the compute units busy. However, waiting for a batch to fill up introduces waiting latency. If you wait too long for a batch to reach its size limit, your tail-latency metrics (p99) will skyrocket. Conversely, if you process batches too eagerly, you sacrifice throughput.

The goal of dynamic batching is to find the "sweet spot" where the marginal gain in throughput from adding another request to the batch is balanced against the marginal increase in latency for all requests in that batch.

Key Components of a Dynamic Batcher

A robust dynamic batching system typically consists of three main components: a request queue, a scheduler, and a latency constraint manager.

• The Queue: Holds incoming inference requests. It should be lightweight and fast, often implemented using a priority queue or a simple FIFO buffer.
• The Scheduler: Decides which requests to include in the next batch. Advanced schedulers might prioritize shorter requests or high-priority users.
• Latency Manager: The "stop" condition. It ensures that if a request has been waiting too long, it is forcibly included in the next batch, even if the batch isn't full.

Implementation Strategy: Timeout-Based Batching

The most common and effective strategy is timeout-based batching. The system waits for a batch to either reach a maximum size ($N_{max}$) or for the oldest request to exceed a maximum wait time ($T_{max}$), whichever comes first.

Here is a simplified conceptual implementation in Python demonstrating this logic:

class DynamicBatcher:
    def __init__(self, max_batch_size=32, max_wait_time_ms=50):
        self.max_batch_size = max_batch_size
        self.max_wait_time_ms = max_wait_time_ms
        self.request_queue = []

    def add_request(self, request):
        self.request_queue.append(request)

    def get_next_batch(self):
        batch = []
        if not self.request_queue:
            return batch
        
        # Sort by arrival time to handle FIFO priority
        self.request_queue.sort(key=lambda x: x.arrival_timestamp)
        
        start_time = self.request_queue[0].arrival_timestamp
        
        for request in self.request_queue:
            # Check latency constraint
            current_wait = (now() - request.arrival_timestamp)
            if current_wait > self.max_wait_time_ms:
                break
                
            # Check size constraint
            if len(batch) >= self.max_batch_size:
                break
                
            batch.append(request)
            
        # Remove processed requests from queue
        # (Implementation details omitted for brevity)
        return batch

Advanced Optimization: Speculative Decoding and KV Cache Management

While batching handles input processing efficiently, decoding is often the bottleneck. When batching diverse prompt lengths, the GPU idle time during the decoding phase increases. Advanced systems now combine dynamic batching with speculative decoding, where a smaller "draft" model proposes tokens for the larger model to verify. This reduces the number of sequential decoding steps, allowing the dynamic batcher to maintain high throughput even with strict latency constraints.

Conclusion

Dynamic batching is not just a feature; it is a necessity for scalable LLM infrastructure. By carefully tuning the maximum batch size and the maximum wait time, engineers can tailor their serving infrastructure to specific Service Level Objectives (SLOs). Whether you are serving a chatbot requiring sub-100ms latency or a background summarization service where throughput is king, understanding the interplay between batching and latency is key to successful deployment.