How vLLM Works
Serving an LLM is a memory management problem. Each request needs a KV cache that grows with sequence length, and naive allocation wastes 60-80% of GPU memory. vLLM introduced PagedAttention — applying operating system virtual memory concepts to attention caches.
What vllm Does
High-throughput memory-efficient LLM inference and serving engine with PagedAttention
vLLM is a production-grade inference engine that efficiently serves large language models with innovations like PagedAttention for memory management, continuous batching, and optimized CUDA kernels. The codebase includes both a Python API and OpenAI-compatible server endpoints for LLM serving.
Architecture Overview
vllm is organized into 4 layers, with 10 components and 13 connections between them.
How Data Flows Through vllm
Requests flow through the scheduler, get batched and executed by the model executor using paged attention, with results streamed back through the serving layer
1Request Ingestion
HTTP requests parsed into internal request objects
2Scheduling
Requests queued and batched by scheduler based on memory and compute constraints
3Memory Allocation
KV cache blocks allocated using PagedAttention memory management
4Model Execution
Batched forward pass through transformer model with custom CUDA kernels
5Token Generation
Sampling and decoding of output tokens using specified generation parameters
6Response Streaming
Generated tokens streamed back to clients in real-time
System Dynamics
Beyond the pipeline, vllm has runtime behaviors that shape how it responds to load, failures, and configuration changes.
Data Pools
KV Cache
Paged memory storing key-value tensors for attention computation
Type: cache
Request Queue
Pending inference requests awaiting scheduling and execution
Type: queue
Block Manager
Tracks allocation and usage of memory blocks for sequences
Type: state-store
Feedback Loops
Memory Pressure Adaptation
Trigger: High memory usage or allocation failures → Scheduler reduces batch size and preempts low-priority requests (exits when: Memory usage returns to acceptable levels)
Type: auto-scale
Continuous Batching
Trigger: New requests arrive or sequences complete → Scheduler rebatches requests and updates execution plan (exits when: No pending requests remain)
Type: recursive
Sequence Preemption
Trigger: Memory allocation fails for new requests → Preempt running sequences to free memory blocks (exits when: Sufficient memory becomes available)
Type: circuit-breaker
Control Points
Max Model Length
GPU Memory Utilization
Max Num Seqs
Enable Chunked Prefill
Quantization Method
Delays
CUDA Graph Capture
Duration: startup
Streaming Response
Duration: per-token
KV Cache TTL
Duration: configurable
Technology Choices
vllm is built with 8 key technologies. Each serves a specific role in the system.
Key Components
- LLMEngine (service): Core inference engine that orchestrates model execution, memory management, and request scheduling
- PagedAttention (module): Memory-efficient attention mechanism using paged memory management for KV cache
- ModelExecutor (service): Loads and executes LLM models with support for various architectures and quantization schemes
- AsyncLLMEngine (service): Asynchronous wrapper around LLMEngine for non-blocking inference requests
- OpenAIServingChat (handler): OpenAI-compatible chat completions API endpoint implementation
- CacheEngine (service): Manages KV cache allocation and memory using PagedAttention algorithm
- Scheduler (service): Schedules and batches inference requests for optimal throughput and latency
- ParallelConfig (config): Configuration for tensor, pipeline, and data parallelism across multiple GPUs
- QuantizationConfig (config): Configuration for various quantization schemes including GPTQ, AWQ, and FP8
- RotaryEmbedding (module): Implements rotary positional embeddings with optimized CUDA kernels
Who Should Read This
ML engineers deploying LLMs in production, or infrastructure teams evaluating inference serving frameworks.
This analysis was generated by CodeSea from the vllm-project/vllm source code. For the full interactive visualization — including pipeline graph, architecture diagram, and system behavior map — see the complete analysis.
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Frequently Asked Questions
What is vllm?
High-throughput memory-efficient LLM inference and serving engine with PagedAttention
How does vllm's pipeline work?
vllm processes data through 6 stages: Request Ingestion, Scheduling, Memory Allocation, Model Execution, Token Generation, and more. Requests flow through the scheduler, get batched and executed by the model executor using paged attention, with results streamed back through the serving layer
What tech stack does vllm use?
vllm is built with PyTorch (Core ML framework with custom CUDA ops integration), CUDA/C++ (Performance-critical kernels for attention and quantization), Ray (Distributed execution and multi-node serving), FastAPI (HTTP API server for OpenAI-compatible endpoints), Triton (GPU kernel development for attention and layer operations), and 3 more technologies.
How does vllm handle errors and scaling?
vllm uses 3 feedback loops, 5 control points, 3 data pools to manage its runtime behavior. These mechanisms handle error recovery, load distribution, and configuration changes.
How does vllm compare to litellm?
CodeSea has detailed side-by-side architecture comparisons of vllm with litellm. These cover tech stack differences, pipeline design, and system behavior.