Dynamic batching is a software technique used in artificial intelligence systems that collects incoming user requests into a group—or batch—and processes them together, triggering the computation either when the batch reaches a maximum size or when a specific time limit expires. This approach allows servers to process multiple requests simultaneously without forcing the first user in line to wait indefinitely for the batch to fill up.
When an artificial intelligence model processes data to generate a response, a process known as inference, the most time-consuming step isn't usually the math itself. The real bottleneck is moving the massive mathematical rules—the model's weights—from the computer's memory into its processing chips. Because moving these weights takes so much time, it is incredibly inefficient to load them just to process a single user's request. It is much faster to load the weights once and apply them to ten, fifty, or a hundred requests at the exact same time.
This is the core idea behind batching. If processing a single request is like driving a bus across town with only one passenger, batching is waiting for the bus to fill up before leaving the station. The problem with traditional static batching is that the bus driver refuses to leave until every single seat is taken. If traffic is slow and only three people show up, those three people might sit on the bus for hours waiting for the remaining seats to fill. In the world of AI, this translates to unacceptable delays for the end user.
Dynamic batching solves this problem by giving the bus driver a stopwatch.
The Two Levers of Control
Dynamic batching operates on a simple but powerful "whichever comes first" logic. To configure a dynamic batching system, engineers set two specific parameters that act as competing levers.
The first lever is the maximum batch size. This is the absolute limit on how many requests the system will process at once, usually dictated by the physical memory limits of the graphics processing units (GPUs) running the model. If the maximum batch size is set to 64, the system will instantly trigger the computation the moment the 64th request arrives, regardless of any other factors.
The second lever is the wait window, sometimes called the batch timeout or maximum queue delay. This is a timer that starts ticking the moment the first request arrives in an empty queue. It represents the absolute longest time the system is willing to make a user wait before processing their request. If the wait window is set to 50 milliseconds, the system will trigger the computation exactly 50 milliseconds after the first request arrives, even if only three requests have been collected.
These two levers work together to handle the unpredictable nature of real-world internet traffic. During a massive spike in traffic, requests pour in so quickly that the maximum batch size is reached in just a few milliseconds. The wait window never has a chance to expire, and the system operates at maximum efficiency, processing full batches as fast as the hardware allows.
During a quiet period, requests might trickle in slowly. The wait window expires long before the batch is full, triggering the computation with only a handful of requests. The system sacrifices some hardware efficiency, but it guarantees that the few users who are online receive their responses quickly. The system dynamically adapts to the traffic, hence the name.
The Throughput vs. Latency Trade-Off
The central challenge of configuring dynamic batching is balancing two competing metrics: throughput and latency.
Throughput measures how much total work the system can accomplish in a given amount of time, usually expressed as requests per second. Latency measures how long an individual user has to wait for their specific response. In almost all computing systems, these two metrics are locked in a tug-of-war.
If an engineer wants to maximize throughput to keep server costs as low as possible, they will set a very long wait window. This gives the system plenty of time to collect a massive batch of requests, ensuring the GPUs are always doing as much work as possible. The downside is that the first user in the batch might have to wait a full second before the computation even begins.
If the engineer wants to minimize latency to provide a snappy, responsive user experience, they will set a very short wait window—perhaps just 5 or 10 milliseconds. Users get their answers almost instantly, but the system will frequently process tiny, inefficient batches, requiring the company to buy more servers to handle the overall traffic volume.
Finding the perfect balance requires careful monitoring of service-level agreements (SLAs). When a company promises its users that an AI feature will respond in under two seconds, that promise is an SLA. Engineers typically focus on the 95th or 99th percentile latency—meaning the experience of the unluckiest 5% or 1% of users—rather than the average. If the average response time is half a second, but 5% of users are waiting ten seconds because their requests got stuck in a poorly configured batching queue, the system is failing its SLA.
A well-tuned dynamic batching system might use a 20-millisecond wait window, which is entirely imperceptible to a human user but provides enough time for a busy server to collect a highly efficient batch of requests (Baseten, 2025). This tiny window acts as a safety net. During a massive spike in traffic, the server might receive a hundred requests in a single millisecond, instantly filling the batch and triggering the computation. The 20-millisecond window never even comes close to expiring. But during a quiet period, when requests are trickling in one by one, that 20-millisecond window ensures that the first user in line doesn't sit there indefinitely while the server waits for more traffic. The system sacrifices a tiny bit of hardware efficiency to guarantee a snappy user experience.
Where Dynamic Batching Excels
While dynamic batching is a powerful technique, it is not the right tool for every AI model. It excels in scenarios where the model produces a fixed or highly predictable amount of output for every request.
One of the best use cases for dynamic batching is embedding models. These models take a piece of text and convert it into a fixed-length list of numbers, which is essential for search engines and recommendation systems. Because the output is always the exact same size regardless of the input, every request in a batch takes the exact same amount of time to process. A library like Batched can seamlessly add dynamic batching to embedding models, resulting in up to a 10x improvement in total throughput without requiring complex architectural changes (Mixedbread, 2024).
Image generation models, like Stable Diffusion, are another perfect fit. Generating a 1024x1024 pixel image takes a predictable amount of computational effort. If a server receives ten requests for images at the same time, it can batch them together, process them simultaneously, and return all ten images at the exact same moment.
Classification models, which simply categorize an input (like determining if an email is spam or not), also benefit massively from dynamic batching. The output is just a single category label, so the computation time is highly uniform across all requests.
The Variable Length Problem
The limitations of dynamic batching become apparent when dealing with models that produce highly variable outputs, such as Large Language Models (LLMs) that generate text.
When you ask an LLM a question, it generates the answer one word—or token—at a time. If you ask for a simple "yes or no" answer, the model might finish in three tokens. If you ask it to write a five-paragraph essay, it might take five hundred tokens.
If a server uses standard dynamic batching for an LLM, it groups the "yes or no" request and the "essay" request into the same batch. Because the GPU processes the entire batch in lockstep, the batch cannot finish until the longest request is complete. The "yes or no" request finishes in a fraction of a second, but it remains locked in the batch, taking up valuable memory space while the GPU spends the next several seconds generating the rest of the essay. This creates massive inefficiencies and idle compute time.
This is why modern LLM servers use a more advanced technique called continuous batching, which operates at the token level rather than the request level. Continuous batching can eject the "yes or no" request the moment it finishes and instantly slot a new user's request into the empty space, keeping the GPU fully utilized. Dynamic batching remains the gold standard for fixed-output models, but continuous batching has largely replaced it for text generation.
Bucketing and Padding
Even when dealing with fixed-output models, dynamic batching still has to handle variable-length inputs. For example, in speech recognition models like Whisper, users might upload audio clips ranging from a three-second voice memo to a five-minute podcast excerpt.
Neural networks require all inputs in a batch to be the exact same size. To achieve this, the system must use padding—adding empty, meaningless data (like silence in an audio file or blank spaces in text) to the shorter inputs until they match the length of the longest input in the batch. The GPU still has to process this empty padding, which wastes computational power. If a three-second audio clip is batched with a five-minute clip, the system wastes massive amounts of energy processing four minutes and fifty-seven seconds of pure silence for the shorter clip.
To mitigate this, advanced dynamic batching systems use a strategy called bucketing. Instead of throwing every incoming request into a single queue, the system creates multiple queues, or buckets, based on the length of the input. Short audio clips go into the "short" bucket, medium clips go into the "medium" bucket, and long clips go into the "long" bucket.
The dynamic batching logic (max size or wait window) applies to each bucket independently. By only batching requests of similar lengths together, the system drastically reduces the amount of wasted padding, ensuring the GPU spends its time processing actual data rather than empty space (SpeechBrain, 2024).
This bucketing strategy is particularly important in production environments that handle diverse workloads. Imagine a customer service platform that uses an AI model to analyze incoming audio messages. Some messages are quick, five-second voicemails saying "cancel my subscription," while others are rambling, ten-minute explanations of a complex billing issue. If the system blindly batched these together, the GPU would spend the vast majority of its time processing the empty padding attached to the five-second message while it waited for the ten-minute message to finish. By sorting the incoming requests into buckets first, the dynamic batching system ensures that the short messages are processed together in a lightning-fast batch, while the long messages are grouped into a separate, heavier batch.
The Evolution of Adaptive Systems
As AI infrastructure matures, the rigid parameters of traditional dynamic batching are giving way to more intelligent approaches. Modern serving frameworks, such as BentoML, now feature adaptive batching.
Instead of relying on a human engineer to manually guess the perfect wait window and maximum batch size, adaptive batching algorithms continuously monitor real-time traffic patterns and adjust the parameters on the fly. During a sudden spike in traffic, the algorithm might automatically increase the maximum batch size to prioritize throughput, knowing that the sheer volume of incoming requests will fill the larger batches almost instantly. During a lull, it might shrink the wait window to prioritize latency, ensuring that the few users who are online aren't penalized by the lack of traffic.
The system learns from recent trends in processing time to ensure it is always operating at the optimal intersection of speed and efficiency (BentoML, 2024). If the server detects that it is consistently failing to meet its latency targets, the adaptive algorithm will automatically tighten the wait window, forcing the system to process smaller, faster batches until the backlog clears. This self-correcting behavior removes the need for constant human intervention and allows the infrastructure to scale gracefully as user demand fluctuates throughout the day.
This adaptability is crucial for complex, multi-agent systems like Sandgarden's Sgai AI Factory. When multiple AI agents—such as a developer, reviewer, and designer—are working autonomously to plan, execute, and validate software code, the underlying infrastructure must handle a highly unpredictable stream of requests. By relying on efficient batching strategies, these advanced workflows can operate smoothly and cost-effectively, regardless of how erratic the traffic becomes.
The Efficiency Engine Nobody Talks About
Dynamic batching is rarely discussed outside of engineering circles, but it is a foundational pillar of the modern AI economy. It is the invisible architecture that allows companies to serve complex, computationally heavy models to millions of users without going bankrupt on server costs.
By replacing the rigid rules of static batching with a flexible, time-aware approach, dynamic batching ensures that servers remain highly utilized during peak hours while still providing snappy, responsive experiences during quiet periods. Whether it is generating a batch of images, transcribing a folder of audio files, or calculating the mathematical embeddings of a document library, dynamic batching is the mechanism that keeps the assembly line moving at exactly the right speed.
