Continuous Batching

## What is Continuous Batching? In traditional Large Language Model (LLM) inference, systems often use "static batching." This means the server waits until a specific number of requests arrive or a timer expires before processing them all together in one go. While this improves throughput for identical tasks, it introduces significant latency for individual users. If you are the ninth person in a batch of ten, you must wait for the tenth person to arrive before your request begins processing. This creates a bottleneck where GPUs sit idle while waiting for more data, or conversely, process small batches inefficiently. Continuous batching, also known as iteration-level scheduling, solves this by treating each token generation step as an independent unit of work. Instead of waiting for a full batch to form, the system processes requests as soon as they are ready. When one request finishes generating its response, it is immediately removed from the batch, and a new incoming request is inserted into that same slot without stopping the computation. This ensures the GPU is constantly working at maximum capacity, handling multiple different-length sequences simultaneously without the overhead of starting and stopping kernels. Think of it like a highway toll booth. Static batching is like a bus that only leaves when it is completely full; if the bus is half-empty, it still waits, causing delays. Continuous batching is like a high-speed electronic toll system where cars pass through one by one at full speed. There is no waiting for a "group" to form. Each car (request) enters and exits independently, keeping the flow constant and efficient regardless of traffic volume fluctuations. ## How Does It Work? Technically, continuous batching relies on dynamic memory management and kernel fusion. In standard inference, the KV cache (Key-Value cache) stores previously computed attention states. In static batching, the entire batch must be padded to the length of the longest sequence, wasting compute on padding tokens. With continuous batching, the scheduler manages the KV cache dynamically. At every decoding step: 1. **Scheduling**: The engine checks which sequences have finished. These are evicted from the active batch. 2. **Insertion**: New pending requests are added to the available slots in the batch. 3. **Execution**: The GPU runs a single forward pass for all active sequences. Because sequences vary in length, the system uses techniques like paged attention or variable-length padding to ensure efficient memory usage. This requires sophisticated software orchestration, often implemented in frameworks like vLLM or TGI (Text Generation Inference). The system must track the state of each request individually while performing matrix operations on the combined batch. ```python # Simplified conceptual logic while True: # 1. Process current batch outputs = model.forward(current_batch) # 2. Check for completed sequences finished_indices = [i for i, req in enumerate(current_batch) if req.is_done()] # 3. Remove finished, add new ones remove_requests(finished_indices) add_new_requests_to_slots() # 4. Update KV Cache indices dynamically update_kv_cache_metadata() ``` ## Real-World Applications * **High-Traffic Chatbots**: Services like customer support bots experience bursty traffic. Continuous batching ensures that during peak hours, latency remains low even when hundreds of users send messages simultaneously. * **Real-Time Translation Apps**: Applications requiring near-instantaneous translation benefit from the reduced time-to-first-token (TTFT) and consistent generation speeds provided by continuous processing. * **Code Completion Tools**: IDE plugins that suggest code snippets need to respond quickly to user keystrokes. Continuous batching allows these tools to serve many developers concurrently without perceptible lag. * **Multi-Tenant SaaS Platforms**: AI platforms hosting multiple enterprise clients can mix long-form document summarization with short query answering in the same batch, optimizing resource allocation across diverse workloads. ## Key Takeaways * **Maximizes Utilization**: Keeps GPUs busy by eliminating idle time between batches, leading to higher throughput. * **Reduces Latency**: Users do not wait for a batch to fill; their request starts processing immediately upon arrival. * **Dynamic Efficiency**: Handles variable-length sequences naturally without wasting compute on padding. * **Complex Implementation**: Requires advanced scheduling algorithms and memory management, making it harder to implement than static batching. ## 🔥 Gogo's Insight **Why It Matters**: As LLMs become consumer-facing products, cost efficiency is paramount. Static batching wastes up to 50% of GPU compute on padding and idle waits. Continuous batching directly translates to lower infrastructure costs and better user experience, making it the standard for modern production inference engines. **Common Misconceptions**: Many believe continuous batching eliminates all latency. It reduces *queuing* latency but does not change the fundamental speed of token generation per se. It optimizes the *system*, not the *model*. **Related Terms**: * **PagedAttention**: A memory management technique often used alongside continuous batching to handle fragmented KV caches. * **Time-to-First-Token (TTFT)**: A key metric improved by continuous batching. * **Speculative Decoding**: Another technique to accelerate inference, often compared with batching strategies.