How does vLLM compare to other inference servers like TGI?

vLLM typically leads in raw throughput and memory efficiency due to PagedAttention. TGI (Text Generation Inference) may offer a more batteries-included ecosystem and tighter integration with the Hugging Face Hub. The choice often depends on your specific performance needs and existing stack.

Can I run vLLM without an NVIDIA GPU?

Yes, vLLM has experimental support for AMD ROCm GPUs and Intel XPU (via IPEX). However, NVIDIA CUDA GPUs (especially Ampere and newer) provide the most mature and performant experience.

What is the maximum sequence length vLLM can handle?

With PagedAttention, vLLM can theoretically handle sequences exceeding 100k tokens, provided you have sufficient GPU memory. The practical limit is the available VRAM for storing the KV cache for all active sequences.

Is vLLM suitable for fine-tuning models?

No. vLLM is exclusively designed for high-throughput inference (serving) of already fine-tuned or base models. For training and fine-tuning, use frameworks like PyTorch, DeepSpeed, or Megatron-LM.

How does continuous batching work in vLLM?

Instead of waiting for a batch of requests to complete, vLLM dynamically inserts new requests into the batch as others finish their generation. This keeps GPU utilization constantly high, dramatically increasing overall throughput compared to static batching.

Directory/Coding/AI Infrastructure/vLLM

vLLM

vLLM is a fast and memory-efficient inference and serving engine for Large Language Models (LLMs). It's designed for high-throughput serving with features like PagedAttention for efficient memory management, continuous batching for maximizing GPU utilization, and support for tensor and pipeline parallelism across multiple GPUs. It supports a wide range of transformer models and quantization methods. Targeted at developers, researchers, and engineers deploying LLMs in production or for experimentation. It provides an OpenAI-compatible API server out of the box, making it a drop-in replacement for existing inference APIs. Key technical features include prefix caching, quantization support (AWQ, GPTQ, etc.), and multi-GPU serving.

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Sep 22, 2026

LLM InferencePagedAttentionHigh ThroughputOpenAI APITensor ParallelMulti-GPU

Product information

Everything worth knowing about vLLM

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

High-Throughput Inference

Serves LLMs with optimized memory management via PagedAttention, enabling high-throughput serving by batching requests and efficiently managing key-value cache memory.

Broad Model Support

Supports a wide range of transformer-based models, including popular architectures like LLaMA, Mistral, GPT-NeoX, and Falcon, as well as multi-modal models.

Continuous Batching

Dynamically groups incoming requests into batches to maximize GPU utilization and reduce latency, a core technique for efficient LLM serving.

OpenAI-Compatible API

Provides a drop-in replacement for OpenAI's API server, allowing easy integration with existing applications and tools built for the OpenAI API.

Technical capabilities

Tensor Parallelism & Quantization

Supports tensor parallelism for distributing models across multiple GPUs and various quantization formats (GPTQ, AWQ, GGUF) to reduce memory footprint and increase speed.

PagedAttention Kernel

Implements a custom CUDA kernel for PagedAttention, the core algorithm that enables efficient memory management for the KV cache, preventing memory fragmentation and waste.

Must watch videos

How to Self-Host an LLM: Local AI Inference with vLLM

11:06

Tutorial11:06

How to Self-Host an LLM: Local AI Inference with vLLM

Want to try for yourself? Find the code here → https://ibm.biz/~pDRvDsIfj Want to run an LLM on your own hardware? Cedric Clyburn demonstrates self-hosted AI inference with vLLM, from loading an open-weight model to serving it through an API. Learn how quantization, GPU memory, and model serving shape practical local AI deployments. 00:00 - Why Self-Host an LLM? 00:21 - What Is vLLM and Why It Matters 00:57 - Continuous Batching & Paged Attention Explained 02:16 - Two Ways to Use vLLM: Python vs API Server 02:45 - Choosing Open Models for AI Inference 03:23 - Running Offline Inference with Python 05:21 - Serving LLMs Through an API Endpoint 07:45 - Understanding Quantization and GPU Memory 08:37 - Using Compressed Models Efficiently 10:08 - Deploying and Evaluating AI Models with vLLM Explore more technical tutorials, articles, and learning resources here → https://ibm.biz/~KvxBUv6yY AI was used in the creation of the transcript and metadata for this video. #vllm #selfhostedai #opensource #opensourceai ----------------------------------------------------------------------------------------------------------- Find us on YouTube: 🔵 IBM Developer: https://youtube.com/@ibmdeveloperadvocates 🔵 IBM Technology: https://www.youtube.com/@IBMTechnology 🔵 IBM: https://www.youtube.com/@IBM Follow our Developer social channels: 🟣https://www.linkedin.com/showcase/ibmdeveloper 🟣 https://x.com/ibmdeveloper

What is vLLM? Efficient AI Inference for Large Language Models

4:57

Tutorial4:57

What is vLLM? Efficient AI Inference for Large Language Models

Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdnaVD Learn more about Large Language Models (LLMs) here → https://ibm.biz/BdnaVR 💰 Struggling with a slow and expensive AI infrastructure? Cedric Clyburn explains how VLLM tackles memory fragmentation and latency in serving large language models. Learn how innovations like paged attention optimize GPU resources and accelerate inference for scalable AI solutions! 🚀 AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdnaVF #ai #llm #gpu #aidevelopment

15:17

Review15:17

Understanding vLLM with a Hands On Demo

Who uses it

Real use cases, no hype

High-Throughput LLM Serving

Deploy and serve large language models at scale with maximum throughput for production APIs.

Real-Time Chat Applications

Power interactive chatbots with low-latency, OpenAI-compatible endpoints.

Batch Inference Pipelines

Process large datasets of text generation tasks efficiently via batched requests.

Multi-Modal AI Deployment

Serve vision-language models (e.g., LLaVA) for integrated image and text understanding.

Cost-Effective Model Serving

Utilize quantization and memory optimization to run large models on consumer or prosumer GPUs.

What's great

State-of-the-art throughput for serving large models
Seamless OpenAI API compatibility
Multi-GPU & tensor parallelism out-of-the-box
Handles multi-modal models (vision-language)

Technical strengths

Blazing fast LLM inference with PagedAttention
Supports latest quantization (AWQ, GPTQ, FP8) & speculative decoding
Highly optimized CUDA kernels for attention & GEMM

Where it falls short

Steep GPU memory & VRAM requirements
Complex initial setup for custom model architectures
Community support can be slow for niche edge cases
Documentation sometimes lags behind rapid release cycles

Technical limitations

Hardware-specific optimizations can be fragile across CUDA versions
Debugging kernel-level issues requires deep CUDA expertise

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

vLLM introduced PagedAttention, a technique that manages key-value (KV) cache memory similarly to an operating system's virtual memory. This eliminates memory fragmentation and waste, enabling massive throughput gains and support for very long sequences.