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
Optimizes LLM serving by managing GPU memory efficiently and batching requests for high throughput
vLLM is a high-throughput inference engine that uses PagedAttention to manage key-value cache memory like virtual memory paging, enabling continuous batching and efficient serving of large language models. The system transforms raw text requests into tokenized batches, executes model inference with optimized kernels, and returns generated text through multiple API interfaces.
Architecture Overview
vllm is organized into 3 layers, with 8 components and 0 connections between them.
How Data Flows Through vllm
Text requests enter through API endpoints and get tokenized into input_ids tensors. The scheduler groups these into batches while the CacheEngine allocates paged memory blocks for attention caches. ModelRunner executes forward passes using optimized kernels, producing logits that get sampled into tokens. Generated tokens are detokenized back to text and streamed to clients, with KV caches persisting across generation steps for efficiency.
1Parse and validate requests
FastAPI server in api_server.py receives HTTP requests, validates them against OpenAI schemas, and converts them into SequenceGroup objects with SamplingParams
2Tokenize input text
TokenizerGroup.encode() converts prompt strings into input_ids tensors using the model's tokenizer, handling special tokens and truncation
3Schedule batch execution
Scheduler.schedule() selects sequences for execution based on memory availability, implements continuous batching by mixing prefill and decode requests
4Allocate KV cache blocks
CacheEngine allocates paged memory blocks for attention keys and values, creating block_tables to map sequence positions to physical memory
5Prepare model inputs
ModelRunner._prepare_model_input() assembles input_ids, positions, and attention metadata into ModelInputForGPU format
6Execute forward pass
ModelRunner.execute_model() runs the transformer forward pass with optimized attention kernels, updating KV caches and producing logits
7Sample next tokens
Sampler applies temperature, top-p, and other sampling parameters to logits, selecting next tokens according to SamplingParams configuration
8Update sequences and detokenize
Engine appends sampled tokens to sequences, detokenizes new tokens to text, and determines if generation is complete based on stop conditions
System Dynamics
Beyond the pipeline, vllm has runtime behaviors that shape how it responds to load, failures, and configuration changes.
Data Pools
KV Cache Blocks
Paged memory blocks storing attention keys and values, managed like virtual memory with allocation and deallocation
Type: buffer
Request Queue
FIFO queue of pending SequenceGroup objects waiting for execution
Type: queue
Running Sequences
Active sequences currently being processed, tracked with generation state and resource allocation
Type: state-store
Model Weights Cache
Cached transformer weights and LoRA adapters loaded in GPU memory for efficient access
Type: cache
Feedback Loops
Continuous Batching Loop
Trigger: Engine step timer → Scheduler selects next batch of sequences, executes forward pass, updates sequences with new tokens (exits when: All requests completed)
Type: polling
Memory Pressure Control
Trigger: KV cache memory full → Scheduler delays new request processing and may preempt running sequences (exits when: Memory available)
Type: backpressure
Generation Stopping
Trigger: Token matches stop criteria or max length reached → Mark sequence as finished and free its resources (exits when: Sequence removed from running set)
Type: convergence
Control Points
max_model_len
gpu_memory_utilization
max_num_seqs
enable_chunked_prefill
attention_backend
Delays
Model Loading
Duration: 10-60 seconds
CUDA Graph Capture
Duration: 5-10 seconds
KV Cache Allocation
Duration: microseconds
Request Queuing
Technology Choices
vllm is built with 8 key technologies. Each serves a specific role in the system.
Key Components
- AsyncLLMEngine (orchestrator): Coordinates the entire inference pipeline by managing request queues, scheduling execution steps, and returning results asynchronously
- Scheduler (scheduler): Implements continuous batching by selecting which sequences to execute next based on memory constraints and scheduling policies
- CacheEngine (allocator): Manages GPU memory for KV cache using PagedAttention by allocating, tracking, and freeing memory blocks
- ModelRunner (executor): Executes the actual model forward pass by preparing inputs, running inference, and sampling next tokens
- FlashAttention (processor): Optimized attention computation using FlashAttention kernels with PagedAttention for memory efficiency
- TokenizerGroup (transformer): Handles text tokenization and detokenization across multiple worker processes for parallel processing
- WorkerBase (executor): Abstracts model execution across different devices and distributed setups, managing model loading and execution
- LoRAManager (adapter): Manages multiple LoRA adapters by loading, caching, and applying them dynamically during inference
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?
Optimizes LLM serving by managing GPU memory efficiently and batching requests for high throughput
How does vllm's pipeline work?
vllm processes data through 8 stages: Parse and validate requests, Tokenize input text, Schedule batch execution, Allocate KV cache blocks, Prepare model inputs, and more. Text requests enter through API endpoints and get tokenized into input_ids tensors. The scheduler groups these into batches while the CacheEngine allocates paged memory blocks for attention caches. ModelRunner executes forward passes using optimized kernels, producing logits that get sampled into tokens. Generated tokens are detokenized back to text and streamed to clients, with KV caches persisting across generation steps for efficiency.
What tech stack does vllm use?
vllm is built with PyTorch (Core tensor computation framework providing CUDA kernels and automatic differentiation), FastAPI (HTTP server framework for OpenAI-compatible API endpoints with async request handling), Triton (GPU kernel compiler for custom CUDA operations like attention and quantization kernels), FlashAttention (Memory-efficient attention implementation that fuses operations and uses tiling), Transformers (Model architecture definitions and tokenizers from HuggingFace ecosystem), and 3 more technologies.
How does vllm handle errors and scaling?
vllm uses 3 feedback loops, 5 control points, 4 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.