Glossary

vLLM

An open-source, high-throughput inference serving engine that implements PagedAttention and continuous batching for efficient LLM deployment.

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INFERENCE SERVING ENGINE

What is vLLM?

vLLM is an open-source library designed to serve large language models with high throughput and low memory footprint using PagedAttention and continuous batching.

vLLM is a high-throughput, memory-efficient inference serving engine for large language models. It implements PagedAttention, a novel attention algorithm that manages the KV cache in non-contiguous memory blocks, virtually eliminating fragmentation and enabling near-zero waste in GPU memory.

The engine leverages continuous batching to dynamically append new requests to a running batch, maximizing GPU utilization and tokens per second (TPS). vLLM supports tensor parallelism for distributed inference and offers seamless integration with popular model weights, making it a foundational tool for self-hosted, sovereign AI infrastructure.

HIGH-THROUGHPUT LLM SERVING

Key Features of vLLM

vLLM is an open-source inference engine that achieves state-of-the-art serving throughput through innovative memory management and scheduling. These are the core architectural components that differentiate it from traditional serving solutions.

PagedAttention

An attention algorithm that manages the KV cache in non-contiguous blocks, analogous to virtual memory paging in operating systems. This eliminates internal and external memory fragmentation, allowing vLLM to serve more sequences concurrently without running out of GPU memory.

Partitions KV cache into fixed-size blocks that can be allocated anywhere in memory
Enables memory sharing between sequences for identical prompt prefixes
Reduces memory waste from ~40% in traditional systems to under 4%
Critical for achieving high batch sizes during inference

EXPLORE

Continuous Batching

A dynamic scheduling technique that appends new requests to the running batch at each iteration rather than waiting for the entire batch to complete. This maximizes GPU utilization by preventing idle compute cycles.

Traditional batching waits for the longest sequence to finish before accepting new work
Continuous batching injects new sequences immediately as old ones complete
Achieves 10x higher throughput compared to static batching under variable load
Works in tandem with PagedAttention for seamless memory reclamation

Prefix Caching

An optimization that automatically detects and reuses the computed KV cache for shared prompt prefixes across multiple requests. When many users share a common system prompt, vLLM computes it once and reuses the result.

Eliminates redundant computation for identical prompt beginnings
Particularly effective for multi-turn conversations and few-shot examples
Operates transparently without user configuration
Reduces Time To First Token (TTFT) for cached prefixes to near-zero

Tensor Parallelism

A distributed inference strategy that splits individual weight matrices across multiple GPUs, enabling vLLM to serve models too large for a single accelerator. Unlike pipeline parallelism, tensor parallelism distributes each layer's computation.

Supports megatron-style sharding across 2, 4, or 8 GPUs
Communication uses NVIDIA NCCL for high-bandwidth GPU-to-GPU transfers
Enables serving models like Llama 3 70B on commodity multi-GPU nodes
Works with both eager mode and CUDA graph execution

Quantization Support

Native integration with multiple weight compression formats to reduce memory footprint and accelerate inference without significant accuracy loss. vLLM supports quantization methods that operate at the kernel level for maximum performance.

AWQ and GPTQ for 4-bit weight-only quantization
FP8 for native 8-bit floating point on H100 GPUs
BitsAndBytes for on-the-fly quantization during loading
Reduces model memory requirements by up to 75% while preserving output quality

OpenAI-Compatible API

A drop-in replacement server that implements the OpenAI completions and chat completions API protocols. Existing applications built for the OpenAI SDK can switch to a self-hosted vLLM endpoint by changing only the base URL.

Supports /v1/completions and /v1/chat/completions endpoints
Implements streaming responses via server-sent events
Compatible with the official openai Python client library
Enables seamless migration from proprietary APIs to self-hosted infrastructure

VLLM DEEP DIVE

Frequently Asked Questions

Technical answers to the most common questions about deploying and optimizing the vLLM inference engine for production-grade large language model serving.

vLLM is an open-source, high-throughput inference serving engine that implements PagedAttention and continuous batching to efficiently serve large language models (LLMs). It works by managing the KV cache in non-contiguous memory blocks, analogous to virtual memory paging in operating systems, which eliminates fragmentation and allows memory sharing across sequences. When a request arrives, vLLM dynamically allocates GPU memory blocks for the prompt's key-value tensors. During autoregressive generation, it appends new blocks as needed. The scheduler continuously batches incoming requests—injecting new sequences into the running batch without waiting for existing generations to complete—maximizing GPU utilization. This architecture achieves up to 24x higher throughput than traditional static batching systems like Hugging Face Transformers, making it the de facto standard for production LLM serving at organizations ranging from startups to hyperscale cloud providers.

Enabling Efficiency, Speed & Accuracy

Intelligent Analysis, Decision & Execution

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VLLM ECOSYSTEM

Related Terms

Mastering vLLM requires understanding the adjacent technologies that enable high-throughput, memory-efficient serving of large language models on private infrastructure.