Continuous Batching

vLLM is an open-source, high-throughput inference and serving engine for large language models. Its defining innovation is PagedAttention, an algorithm that manages the Key-Value (KV) Cache like an operating system manages virtual memory. This allows for non-contiguous storage of cached keys and values, drastically reducing memory fragmentation and enabling efficient continuous batching even with highly variable sequence lengths. vLLM achieves state-of-the-art throughput and is a foundational component in many production LLM serving stacks.

• Core Innovation: PagedAttention for efficient KV cache management.
• Key Benefit: High throughput and reduced memory waste.
• Use Case: High-volume, variable-request LLM serving.

Text Generation Inference (TGI) is Hugging Face's optimized toolkit for deploying and serving LLMs. It implements continuous batching (which it calls continuous or iterative batching) alongside other performance optimizations like flash attention and custom CUDA kernels. TGI supports token streaming via Server-Sent Events (SSE), safetensors for secure weight loading, and extensive metrics for observability. It is the serving backend for Hugging Face's Inference Endpoints and is designed for robust, scalable production deployment.

• Key Features: Continuous batching, token streaming, flash attention.
• Ecosystem: Tight integration with the Hugging Face Hub.
• Use Case: Enterprise-grade, full-featured LLM serving.

Triton Inference Server is a versatile, open-source serving software that supports models from multiple frameworks (PyTorch, TensorFlow, ONNX). For LLMs, it supports dynamic batching and, through its BLS (Business Logic Scripting) and ensemble models, can orchestrate complex pipelines that approximate continuous batching logic. While its native dynamic batching groups requests before execution, its high concurrency model and support for stateful models (to manage KV cache across requests) make it a powerful option for integrating LLM inference into broader, multi-model ML pipelines.

• Strength: Multi-framework support and pipeline orchestration.
• Batching: Native dynamic batching with stateful extensions.
• Use Case: Complex inference pipelines with mixed model types.

TensorRT-LLM is NVIDIA's SDK for compiling and optimizing LLMs for high-performance inference on NVIDIA GPUs. It includes a runtime with in-flight batching, NVIDIA's term for continuous batching. The workflow involves compiling a model from a framework like PyTorch into a highly optimized TensorRT engine. This engine, combined with the runtime's in-flight batching scheduler, delivers minimal latency and maximum throughput. It is particularly effective when used end-to-end with Triton Inference Server via a TensorRT-LLM backend.

• Core Technology: Compiles models to optimized TensorRT engines.
• Batching: In-flight batching (continuous batching) runtime.
• Use Case: Maximum performance on NVIDIA hardware stacks.

SGLang is a domain-specific language and runtime system designed to orchestrate the execution of complex LLM programs involving multiple generation calls, tool use, and control flow. The LMSYS SGLang runtime provides the backend execution engine that implements radix attention for efficient KV cache reuse across multiple calls within a session and automatic parallelization of operations. Its runtime scheduler naturally enables continuous batching across different requests executing various LLM programs, making it highly efficient for agentic workloads and interactive applications beyond simple chat completions.

• Paradigm: DSL for complex LLM programs (chains, tools, branches).
• Optimization: Radix attention for KV cache reuse across a session.
• Use Case: Agentic systems, complex reasoning, and interactive apps.

For maximum flexibility, teams can build custom continuous batching systems on general-purpose orchestration frameworks. Ray Serve is a scalable model serving library built on Ray that allows developers to implement custom batching policies in Python. By creating an async __call__ method that queues requests, a custom scheduler can implement continuous batching logic tailored to specific needs, such as complex priority schemes or integration with unique multi-adapter serving setups. This approach offers deep control but requires significant engineering investment.

• Flexibility: Full control over batching and scheduling logic.
• Foundation: Built on Ray for distributed computing.
• Use Case: Highly customized serving requirements or research prototypes.

A high-throughput, open-source inference and serving engine for large language models. It is a canonical implementation of continuous batching, paired with its innovative PagedAttention algorithm for optimal KV cache memory management.

• Core Innovation: PagedAttention treats the KV cache like virtual memory, allowing flexible, non-contiguous storage that minimizes waste and enables very large batch sizes.
• Performance: Enables significantly higher request throughput compared to basic dynamic batching by efficiently handling variable-length sequences in a continuous batch.

An open-source toolkit from Hugging Face for deploying and serving LLMs. It provides a production-ready server that implements continuous batching (which it calls continuous batching or iteration-level batching) and token streaming.

• Framework Support: Optimized for Hugging Face Transformers models.
• Features: Includes built-in tokenizers, Prometheus metrics, and support for parameter-efficient fine-tuning methods like LoRA and adapters.

A versatile, open-source inference serving software from NVIDIA that supports multiple frameworks (PyTorch, TensorFlow, ONNX). It provides a sophisticated dynamic batcher and supports ensemble models for complex pipelines.

• Model Concurrency: Can run multiple models or different instances of the same model concurrently on the same GPU.
• Custom Batching: Offers granular control over batching logic, including preferred batch sizes and maximum queue delays, which can be configured to enable continuous batching behavior.