vLLM

In-Depth Analysis: Unpacking the Secrets Behind vLLM’s High-Throughput Inference System

Honghao Wang

26 Oct 2025 — 4 min read

Introduction

In today's fast-paced development of large model applications, both research and industry focus on improving inference speed and efficiency.

vLLM has emerged as a high-performance inference framework, optimized for large language model (LLM) inference. It enhances throughput and response speed without compromising accuracy through innovations in:

GPU memory management
Parallel scheduling
KV cache optimization

See the full technical deep dive here: Inside vLLM: Anatomy of a High-Throughput LLM Inference System.

---

Aleksa Gordic — formerly at Google DeepMind and Microsoft — invested extensive effort in understanding vLLM’s codebase. This work could easily be expanded into a full book.

Blog Quick Facts

Title: Inside vLLM: Anatomy of a High-Throughput LLM Inference System
Link: Blog Post

---

Topics Covered in the Blog

Inference engine workflow: request handling, scheduling, paged attention, continuous batching
Advanced features: chunked prefill, prefix caching, guided decoding, speculative decoding, decoupling prefill & decode
Scalability: from 1 GPU small models to trillion-parameter multi-node deployments (TP, PP, SP)
Web deployment: APIs, load balancing, data-parallel coordination, multi-engine architectures
Performance metrics: TTFT, ITL, e2e latency, throughput, GPU roofline model

The blog is packed with examples, diagrams, and visualizations to help you grasp inference engine design.

---

LLM Engine Core Concepts

The LLM Engine is vLLM's core building block:

Offline usage: high-throughput inference without web access
Core components:
Configuration
Input processor
EngineCore client
Output processor

It is extendable to online, async, multi-GPU, multi-node systems.

---

Offline Inference Example

from vllm import LLM, SamplingParams

prompts = ["Hello, my name is", "The president of the United States is"]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

def main():
    llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
    outputs = llm.generate(prompts, sampling_params)

if __name__ == "__main__":
    main()

Environment Variables:

VLLM_USE_V1="1"
VLLM_ENABLE_V1_MULTIPROCESSING="0"

---

Characteristics

Offline: no web/distributed support
Synchronous: single blocking process
Single GPU: no DP/TP/PP/EP
Standard Transformer

Workflow:

Instantiate engine
Call `generate` with prompts

---

Engine Components

Constructor

Configuration (parameters, model, cache)
Processor (validate, tokenize)
EngineCore client
Output processor

EngineCore

Model Executor: drives model computation
Structured Output Manager: for guided decoding
Scheduler: decides execution sequence

---

Scheduler

Policies: FCFS or priority
Queues: waiting & running
KV Cache Manager: central to paged attention, managing `free_block_queue` pool.

---

Worker Initialization

1. Device Init

Select CUDA device
Verify dtype support
Configure parallelism (DP, TP, PP, EP)
Prepare model runner & input batch

2. Load Model

Instantiate architecture
Load weights
Call `.eval()`
Optional: `torch.compile()`

3. KV Cache Init

Get spec for each attention layer
Profiling pass & GPU memory snapshot
Bind cache tensors to layers
Configure attention metadata
Warmup CUDA graphs (if eager not enforced)

---

Generate Function

Step 1: Accept Request

Create request ID
Tokenize prompt
Package metadata
Add to scheduler queue

---

Step Loop: `step()`

Schedule requests
Forward pass
Postprocess outputs
Append tokens
Detokenize
Check stop conditions
KV block cleanup

---

Stop Conditions

Token limits exceeded
EOS token (unless ignored)
Matching stop token IDs
Stop string in output

---

Prefill vs Decode Workloads

Prefill: compute-bound
Decode: memory-bound
V1 Scheduler mixes both in a step

`allocate_slots` controls KV block allocation for both types, based on token count and availability.

---

Forward Pass Flow

Update state
Prepare inputs (to GPU)
Execute model with paged attention & continuous batching
Extract last token state
Sampling (greedy, top-p/k)

Two execution modes:

Eager: direct PyTorch
Captured: CUDA Graph replay

---

Advanced Features

Chunked Prefill

Divide long prompts into smaller chunks to avoid blocking other requests.

---

Prefix Caching

Reuse computed KV blocks for repeated prompt prefixes.

Uses hashing per block and cache hit detection.

---

Guided Decoding (FSM)

Restrict logits at each step to grammar-conforming tokens.

---

Speculative Decoding

Use a draft LM to propose multiple tokens; large LM verifies in one pass.

Methods:

n-gram
EAGLE
Medusa

---

Split Prefill/Decode

Separate workers for compute-bound prefill and latency-sensitive decode, exchanging KV via connectors.

---

Scaling Up

MultiProcExecutor

Enable TP/PP > 1 across GPUs/nodes:

Coordinate via RPC queues
Spawn worker processes
Synchronize tasks and collect results

---

Distributed Deployment

Combine TP and DP across nodes:

Coordinate via DP groups
API server in front for request routing
Engines in headless mode for back-end

---

API Serving

Wrap engine in async client:

Input/output queues
Async tasks for socket communication
FastAPI endpoints (`/completion`, `/chat/completion`)
Load balancing via DP coordinator

---

Benchmarking and Auto-Tuning

Metrics

Latency: TTFT, ITL
Throughput: tokens/sec

Trade-off between batch size and inter-token latency.

---

vLLM CLI:

vllm bench latency --model  --input-tokens 32 --output-tokens 128 --batch-size 8

---

Summary

We explored:

Single-process core engine
Advanced optimizations (chunked prefill, prefix caching, guided/speculative decoding)
Multi-process scaling
Distributed deployment
Benchmarking performance

---

References

---

Pro Tip: If integrating vLLM outputs into real-world applications, consider pairing with AiToEarn — an open-source AI content monetization tool for cross-platform publishing. It works seamlessly alongside inference pipelines, allowing creators to publish and monetize outputs across Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, and X (Twitter). See: AiToEarn GitHub.