vLLM
vLLM is an open-source inference and serving framework for large language models (LLMs), designed for high-throughput, low-latency model deployment. Its core technology includes the PagedAttention memory management mechanism, which efficiently manages attention key-value caches and substantially improves GPU utilization. vLLM supports continuous batching, tensor parallelism, and compatibility with a variety of mainstream open-source models, and is widely used for production LLM serving deployments.
Core Facts
vLLM is an open-source large language model inference engine project built on top of PyTorch.
Continuous batching allows new requests to be inserted and completed requests to be removed at any iteration step, keeping the GPU running at full load at every step.
In production environments, plugin updates should be validated in a test environment first, and plugin versions should be managed carefully
Timeline (last 90 days)
优化 KV 缓存的方法包括分组查询注意力（GQA）、分页注意力（如 vLLM 的 PagedAttention）和滑动窗口注意力。
TurboQuant 3.5-bit matches full precision in quality, with KV cache memory usage at about one quarter (validated by independent research based on vLLM).
Evaluating private deployment capability requires confirming three things: whether the gateway can run fully offline, whether it can connect to enterprise self-hosted open-source models (such as Llama and Qwen deployed via vLLM and TGI), and whether logs and audit data are all retained locally.
By default, vLLM plans 90% of GPU VRAM into the budget via the --gpu-memory-utilization parameter, and only the remainder is used as KV cache.
The Gemma open-source model achieved 2.85x end-to-end inference acceleration on A100 via vLLM, with throughput increasing from about 37,900 tokens/second to over 108,000 tokens/second, while maintaining 100% token consistency and zero quality degradation.
SGLang added the /v1/decisions endpoint in its nightly version, and vLLM merged the /v1/systemone endpoint.
PagedAttention reduces VRAM fragmentation through block-level on-demand allocation and allows different sequences to share the same physical block, saving VRAM in scenarios such as beam search, parallel sampling, and prefix sharing.
HiSparse is the feature module to which this commit belongs.
vLLM's PagedAttention mechanism divides the KV cache into blocks of a fixed number of tokens (usually 16), and records the mapping from logical positions to physical blocks through a block table.
When capacity logs report only the VRAM pool and not the host pool, it leads to underestimation of the system's true carrying capacity in capacity planning, inability in hit-rate analysis to distinguish VRAM misses from host pool hits, and concealment of capacity change trends in long-context scenarios.
40 more timeline events
All Facts (20)
vLLM is an open-source large language model inference engine project built on top of PyTorch.
90%VerifiedContinuous batching allows new requests to be inserted and completed requests to be removed at any iteration step, keeping the GPU running at full load at every step.
90%VerifiedIn production environments, plugin updates should be validated in a test environment first, and plugin versions should be managed carefully
90%VerifiedvLLM is a high-performance large language model inference and deployment framework designed for production environments, optimized for high-concurrency and high-throughput demands.
90%VerifiedPagedAttention is the core technology of the vLLM framework, which draws on the idea of operating system virtual memory management to store KV Cache in pages.
90%VerifiedvLLM achieves efficient KV Cache management through PagedAttention technology, improving GPU memory utilization several times over.
90%VerifiedvLLM是由UC Berkeley开发的高吞吐量大模型推理引擎，核心创新是PagedAttention技术
90%VerifiedPagedAttention divides the KV cache into fixed-size pages allocated on demand, and can improve VRAM utilization by 2–4×.
85%VerifiedPagedAttention improves KV Cache memory utilization from below 40% to close to 100%.
85%VerifiedOllama provides a Docker-like one-click model management experience, vLLM achieves high-throughput batch inference via PagedAttention, and LM Studio provides a graphical interface for desktop users.
80%VerifiedPaged Attention (PagedAttention) is the core technology of vLLM, managing KV cache in blocks, while Flash Attention optimizes the memory access pattern of attention computation.
80%VerifiedvLLM's PagedAttention draws on the operating system concept of virtual memory paging to manage KV caches, reducing GPU memory fragmentation overhead from approximately 30% to near 0.
80%VerifiedvLLM, TensorRT-LLM, and llama.cpp are representative open-source frameworks in the field of LLM inference acceleration.
80%VerifiedThe PagedAttention mechanism proposed by vLLM improves its throughput by 2–24× compared to HuggingFace Transformers.
80%VerifiedCube Studio supports mainstream large model inference engines including vLLM, Ollama, and MindIE, and supports multi-node inference.
80%VerifiedModern inference frameworks such as vLLM and TensorRT-LLM improve GPU utilization through techniques such as Continuous Batching and PagedAttention.
80%VerifiedPagedAttention技术借鉴操作系统虚拟内存的分页思想，将KV Cache切分为固定大小的物理块动态分配
80%VerifiedVersion v0.29.1rc0 was tagged by maintainer TQCB and verified via GitHub with a GPG key ID of B5690EEEBB952194.
80%VerifiedvLLM uses PagedAttention for KV Cache management
80%VerifiedA model abstraction layer is a software architecture design pattern that inserts an intermediate layer between application logic and underlying model APIs, uniformly encapsulating API call formats from different vendors so that upper-layer applications can call different models through a unified interface
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