This page provides a high-level introduction to vLLM's architecture, core components, and design principles. It serves as an entry point for understanding how vLLM orchestrates large language model inference across multiple hardware platforms with optimized memory management and execution.
vLLM V1 represents a significant re-architecture of the core engine (scheduler, KV cache manager, worker, and sampler) to provide a cohesive, modular, and high-performance framework while retaining the stable model implementations and kernels from V0.
vLLM is a fast and easy-to-use library for LLM inference and serving. It optimizes throughput and memory efficiency through several key technologies:
vLLM follows a layered architecture with a clear separation of concerns. The V1 engine introduces a decoupled execution model where the EngineCore runs in a separate process from the API frontend to minimize CPU overhead and Python GIL contention vllm/v1/engine/core_client.py84-105
Title: "vLLM System Architecture (Natural Language to Code Entities)"
Sources: vllm/engine/arg_utils.py35-65 vllm/config/vllm.py54-122 vllm/config/model.py122-167 vllm/v1/engine/core.py103-113 vllm/v1/engine/core_client.py71-132 vllm/v1/engine/input_processor.py44-46 vllm/v1/engine/output_processor.py45-47 vllm/v1/core/sched/scheduler.py69-80 vllm/v1/kv_cache_interface.py22-36 vllm/entrypoints/llm.py67-75 vllm/v1/engine/async_llm.py72-89
Layered Architecture Overview
| Layer | Purpose | Key Components |
|---|---|---|
| External Interface | Entry points for users | LLM, AsyncLLM, OpenAI API server vllm/entrypoints/llm.py67 vllm/v1/engine/async_llm.py72 vllm/entrypoints/openai/api_server.py109-137 |
| Configuration | Argument parsing and config assembly | EngineArgs, VllmConfig, ModelConfig, ParallelConfig vllm/engine/arg_utils.py35 vllm/config/vllm.py54 |
| Engine Orchestration | Request lifecycle and IPC coordination | EngineCore, InputProcessor, OutputProcessor vllm/v1/engine/core.py103 vllm/v1/engine/input_processor.py44 vllm/v1/engine/async_llm.py141 |
| Scheduling & Memory | Resource allocation and KV management | Scheduler, KVCacheManager, KVCacheConfig vllm/v1/core/sched/scheduler.py69 vllm/v1/core/kv_cache_manager.py46 vllm/v1/kv_cache_interface.py27 |
| Execution | Model forward passes on hardware | Executor, Worker vllm/v1/executor/__init__.py79 vllm/v1/worker/worker_base.py41 |
Sources: vllm/v1/engine/core.py103-168 vllm/v1/engine/async_llm.py72-156 vllm/entrypoints/llm.py67-162 vllm/v1/engine/core_client.py71-132 vllm/engine/arg_utils.py35-120
EngineCore is the high-performance inner loop of vLLM. It manages the Scheduler and the Executor vllm/v1/engine/core.py103-132 It initializes specialized configurations and hardware-specific optimizations.
Key responsibilities:
_initialize_kv_caches, and sets up the model executor and scheduler vllm/v1/engine/core.py131-168EngineCoreRequest objects containing prompt token IDs, multimodal features, and sampling parameters vllm/v1/engine/core.py64-72step() vllm/v1/engine/core.py326-340Sources: vllm/v1/engine/core.py103-186
EngineCoreClient abstracts the communication between the frontend (API) and the backend (EngineCore) vllm/v1/engine/core_client.py71:
EngineCore in the same process as the caller, typically for offline inference vllm/v1/engine/core_client.py105-108AsyncLLM vllm/v1/engine/core_client.py132-135Sources: vllm/v1/engine/core_client.py71-135 vllm/v1/engine/core.py103-186
The flow below demonstrates how a user request traverses the system from a high-level API call to GPU execution.
Title: "vLLM V1 Request Lifecycle (Code Entity Space)"
Sources: vllm/v1/engine/async_llm.py138-146 vllm/v1/engine/core_client.py143-212 vllm/v1/engine/core.py160-168 vllm/v1/engine/core.py326-340 vllm/v1/core/sched/scheduler.py417-425
Request Lifecycle Stages:
InputProcessor validates SamplingParams or PoolingParams and converts EngineInput into EngineCoreRequest vllm/v1/engine/input_processor.py44-60 vllm/v1/engine/async_llm.py138Scheduler determines which requests enter the current batch based on token budgets and cache availability vllm/v1/core/sched/scheduler.py417-450Executor coordinates workers to run the model forward pass vllm/v1/executor/__init__.py79OutputProcessor collects EngineCoreOutputs, updates request states, and handles detokenization vllm/v1/engine/output_processor.py45-65Sources: vllm/v1/engine/core.py103-186 vllm/v1/engine/async_llm.py135-168 vllm/v1/engine/core_client.py143-212 vllm/v1/core/sched/scheduler.py417-450
vLLM V1 is designed for extreme scale and performance:
EngineCoreClient which handles ZMQ communication vllm/v1/engine/core_client.py71-135ParallelConfig vllm/config/parallel.py65torch.compile and CUDA graph capture modes (CUDAGraphMode) to minimize kernel launch overhead vllm/config/compilation.py53-64StatLoggerManager, providing snapshots of SchedulerStats and IterationStats vllm/v1/metrics/stats.py28-100 vllm/v1/engine/async_llm.py159-169Sources: vllm/v1/engine/core_client.py71-135 vllm/config/compilation.py53-64 vllm/v1/metrics/stats.py28-100 vllm/v1/core/kv_cache_utils.py41-44 vllm/config/parallel.py65