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Introduction:
The economics of deploying large language models (LLMs) at scale have long been plagued by a silent killer: memory fragmentation. Traditional inference systems allocate contiguous memory blocks for each sequence’s key-value (KV) cache, reserving space for maximum possible sequence length regardless of actual usage—a system that wastes up to 97% of reserved capacity for short responses. UC Berkeley researchers have solved this bottleneck with vLLM, an open-source inference engine that rethinks memory allocation through PagedAttention, delivering up to 24x better throughput and transforming the economics of AI deployment. With 85,000 GitHub stars and over 2,800 contributors, vLLM is now powering production workloads at Meta, Mistral AI, Cohere, and IBM.
Learning Objectives:
- Understand PagedAttention’s operating system-inspired memory management and how it eliminates KV cache fragmentation
- Deploy vLLM for production-grade LLM inference with continuous batching and CUDA graph optimization
- Benchmark and tune vLLM performance across single-GPU and distributed environments
1. PagedAttention: Operating System Memory Management for GPUs
Traditional LLM inference suffers from a fundamental inefficiency: each sequence’s KV cache is stored in a contiguous memory block, reserving space for the maximum possible sequence length. A system configured for 4,096 tokens allocates that full memory even for 100-token responses, wasting 97% of reserved capacity. Multiply this by hundreds of concurrent requests, and GPU memory fills with empty reservations while actual sequences queue waiting for resources.
PagedAttention eliminates this waste by partitioning the KV cache of each request into fixed-size “KV Blocks” (typically 16 tokens each). These blocks can be stored in non-contiguous physical memory, just as operating systems map virtual memory to physical pages. Each sequence maintains a list of page references rather than a contiguous allocation, enabling:
- Dynamic allocation: Memory provisions only as sequences grow. The first token allocates one page; the seventeenth triggers a second page allocation.
- Memory sharing: Identical prompt prefixes share KV cache pages across requests. Ten users asking variations of the same system prompt share a single cached copy, reducing memory consumption by up to 90%.
- Near-zero waste: Traditional systems waste an average of 4.1 tokens per sequence in partially filled blocks; PagedAttention reduces waste to fractions of a page.
The translation from logical KV positions to physical GPU memory blocks adds minimal overhead—typically under 8 tokens per sequence regardless of length. The result? Organizations like Stripe have reported a 73% reduction in inference costs after migrating from Hugging Face Transformers to vLLM, processing the same 50 million daily API calls on one-third the GPU fleet.
2. Installation and Environment Setup
vLLM supports NVIDIA GPUs (CUDA), AMD GPUs (ROCm), Apple Silicon (vLLM-Metal), and Google TPUs. For NVIDIA GPUs, installation is straightforward:
Recommended: Use uv for fast Python environment management uv venv source .venv/bin/activate uv pip install vllm Or with conda conda create -1 vllm python=3.12 conda activate vllm pip install vllm For AMD ROCm (Python 3.12, ROCm 7.0, glibc >= 2.35) uv pip install vllm
For a quick test without creating a permanent environment:
uv run --with vllm vllm --help
System requirements: Linux (recommended), Python 3.10–3.13. For production deployments, Docker images are available:
docker pull vllm/vllm-openai:latest Nightly ROCm image docker pull vllm/vllm-openai-rocm:nightly
3. Offline Batched Inference: Your First vLLM Workload
The simplest way to use vLLM is offline batch inference. Create a Python script:
from vllm import LLM, SamplingParams
Initialize the model
llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct")
Define sampling parameters
sampling_params = SamplingParams(temperature=0.7, top_p=0.95, max_tokens=256)
Prepare prompts
prompts = [
"Explain quantum computing in simple terms.",
"Write a Python function to reverse a linked list.",
]
Run inference
outputs = llm.generate(prompts, sampling_params)
Print results
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[bash].text
print(f" {prompt!r}")
print(f"Generated: {generated_text!r}")
This script leverages PagedAttention automatically. Behind the scenes, vLLM’s scheduler forms batches by selecting sequences up to `max_num_seqs` and tokens up to max_num_batched_tokens, dynamically adjusting as requests complete.
4. Deploying the OpenAI-Compatible API Server
vLLM provides an OpenAI-compatible API server, simplifying adoption for teams already using OpenAI’s SDK:
Start the server with a model python -m vllm.entrypoints.openai.api_server \ --model meta-llama/Llama-3.1-8B-Instruct \ --tensor-parallel-size 1 \ --max-model-len 4096 Or using the vLLM CLI (v0.22+) vllm serve meta-llama/Llama-3.1-8B-Instruct \ --port 8000 \ --max-model-len 4096
Once running, you can query it using the OpenAI Python client:
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="EMPTY" vLLM doesn't require an API key
)
response = client.chat.completions.create(
model="meta-llama/Llama-3.1-8B-Instruct",
messages=[{"role": "user", "content": "What is PagedAttention?"}],
temperature=0.7,
max_tokens=512
)
print(response.choices[bash].message.content)
For production, consider these configuration flags:
vllm serve meta-llama/Llama-3.1-8B-Instruct \ --port 8000 \ --max-model-len 8192 \ --gpu-memory-utilization 0.9 \ --max-1um-seqs 256 \ --enable-prefix-caching \ --cuda-graph-mode full
5. Continuous Batching and CUDA Graph Optimization
vLLM’s continuous batching dynamically adjusts batch composition as requests arrive and complete, maximizing GPU utilization. Unlike traditional static batching that waits for all requests in a batch to finish, continuous batching allows new requests to be inserted and completed requests to be removed on the fly.
CUDA Graphs further accelerate execution by capturing GPU operations into a single graph that can be replayed with low overhead. vLLM offers three CUDA graph modes:
- NONE: Disabled—useful for debugging
- PIECEWISE: Most flexible; attention operations remain eager, everything else uses CUDA graphs
- FULL: Captures full CUDA graphs for all batch types; recommended for production when using FlashAttention 3
Enable CUDA graphs in your configuration:
from vllm import LLM llm = LLM( model="meta-llama/Llama-3.1-8B-Instruct", cuda_graph_mode="full", Or "piecewise" max_num_seqs=256, max_num_batched_tokens=4096 )
The CUDA Graph dispatcher automatically selects the appropriate graph for each batch based on composition, ensuring optimal performance without manual tuning.
6. Performance Benchmarking: Real-World Numbers
Independent benchmarks confirm vLLM’s dominance in production environments. In a Red Hat Developer benchmark using a single NVIDIA A100-PCIE-40GB GPU with the Llama-3.1-8B-Instruct model, vLLM achieved a peak throughput of 793 tokens per second (TPS) compared to Ollama’s 41 TPS—a 19x improvement. P99 latency at peak throughput was 80 ms for vLLM versus 673 ms for Ollama.
Against Hugging Face’s TGI, vLLM achieved up to 24x higher throughput under high-concurrency workloads, though TGI demonstrated lower tail latencies for interactive single-user scenarios. For multi-user applications prioritizing throughput and scalability, vLLM delivered more than 35x the request throughput (RPS) and 44x the total output tokens per second compared to llama.cpp at peak load.
To benchmark your own deployment, use GuideLLM, the official benchmarking tool:
Install GuideLLM pip install guidellm Run benchmark with concurrent users guidellm benchmark \ --server vllm \ --model meta-llama/Llama-3.1-8B-Instruct \ --concurrency 32 \ --requests 1000 \ --output results.json
7. Advanced: Automatic Prefix Caching
vLLM’s automatic prefix caching eliminates redundant computation for shared prompt prefixes. Each KV block is uniquely identified by a hash of its tokens and prefix. A global hash table maps logical KV blocks to physical blocks, enabling:
- Shared prefixes: If a new request shares a system prompt with a previous request, the cached KV cache is reused without recomputation
- LRU eviction: When cache is full, vLLM evicts blocks with reference count zero using a least-recently-used policy
Enable prefix caching with:
vllm serve meta-llama/Llama-3.1-8B-Instruct \ --enable-prefix-caching \ --max-1um-seqs 256
This feature is particularly valuable for applications with standardized prompts—RAG systems, chatbots with fixed system instructions, or multi-turn conversations. Production systems with standardized prompts see utilization improvements exceeding 400%.
What Undercode Say:
- Memory fragmentation is the hidden cost of LLM inference. Traditional serving wastes 60–80% of GPU memory due to contiguous KV cache allocation. PagedAttention eliminates this with operating system–inspired paging, delivering 24x better memory efficiency.
-
vLLM is production-ready. With 85,000 GitHub stars, 2,800+ contributors, and deployments at Meta, Mistral AI, Cohere, and IBM, vLLM has proven its reliability at scale. The OpenAI-compatible API reduces adoption friction, while features like continuous batching and CUDA graphs maximize throughput.
Analysis: The implications of vLLM extend beyond mere cost savings. By dramatically improving memory efficiency, vLLM enables organizations to serve larger models on fewer GPUs, democratizing access to state-of-the-art AI. The 73% cost reduction reported by Stripe suggests that vLLM is not just an academic exercise but a transformative tool for AI economics. However, teams must consider the trade-off: while vLLM excels in high-throughput batch processing, Hugging Face TGI may offer lower tail latencies for latency-sensitive interactive applications. The choice ultimately depends on workload characteristics—batch document processing favors vLLM, while real-time chatbots may benefit from alternative optimizations.
Prediction:
- +1 vLLM will become the default inference engine for enterprise LLM deployments within 18–24 months, displacing proprietary solutions as open-source performance continues to close the gap.
- +1 The principles of PagedAttention—virtual memory–style management for GPU workloads—will extend beyond LLM inference to other memory-intensive AI workloads, including diffusion models and video generation.
- -1 The complexity of fine-tuning vLLM for specific hardware (CUDA graph modes, tensor parallelism, prefix caching tuning) creates a skills gap that may slow adoption among smaller teams without dedicated ML engineering resources.
- +1 Automatic prefix caching will enable new classes of applications with massive shared contexts, such as enterprise knowledge bases with 100,000+ token system prompts served at sub-second latencies.
- -1 As vLLM adoption grows, GPU vendors will face pressure to optimize hardware for paged memory access patterns, potentially disrupting the current CUDA-dominated ecosystem.
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