PagedAttention
A core technical innovation in vLLM that manages KV Cache in GPU memory by analogy with the virtual memory paging mechanism of operating systems, substantially improving GPU memory utilization and concurrent throughput.
Core Facts
vLLM is an open-source large language model inference engine project built on top of PyTorch.
PagedAttention 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.
vLLM achieves efficient KV Cache management through PagedAttention technology, improving GPU memory utilization several times over.
Timeline (last 90 days)
PagedAttention makes scenarios such as prefix sharing and parallel sampling more efficient.
By reducing memory waste, PagedAttention allows more requests to reside in GPU memory simultaneously and be executed in batches, thereby significantly improving throughput.
PagedAttention divides the KV cache into fixed-size pages, allocates them on demand, and addresses them through a logical-to-physical mapping table, allowing pages to be stored non-contiguously in GPU memory.
PagedAttention solves the problem of memory-space fragmentation, and continuous batching solves the problem of resource release over time; together they constitute vLLM's throughput advantage.
Paging incurs extra block-table lookup overhead, and in extremely short-sequence scenarios vLLM's PagedAttention advantage is not obvious.
vLLM is known for PagedAttention and ease of use, with an open ecosystem and broad model coverage.
The reason for choosing vLLM over Ollama as the inference engine lies in production features: out-of-the-box continuous batching, PagedAttention and an OpenAI-compatible API.
vLLM originates from UC Berkeley research on PagedAttention (Kwon et al.), and its core goal is to solve the memory waste problem in traditional inference serving.
The compatibility of different quantization schemes with PagedAttention varies and needs to be verified empirically on Kimi K3.
The default KV Cache block size in vLLM is 16.
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All Facts (20)
vLLM is an open-source large language model inference engine project built on top of PyTorch.
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 is a high-throughput large model inference engine developed by UC Berkeley, with PagedAttention technology as its core innovation.
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%VerifiedvLLM uses PagedAttention for KV Cache management
80%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%VerifiedFlashAttention reduces GPU memory usage from being proportional to the square of the sequence length to a linear relationship by redesigning the memory access pattern of the attention computation.
80%VerifiedvLLM employs Continuous Batching, refining the scheduling granularity to each generation step, filling slots as soon as they become available.
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%VerifiedThe PagedAttention mechanism proposed by vLLM improves its throughput by 2–24× compared to HuggingFace Transformers.
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%VerifiedKV Cache avoids redundant computation by caching already-computed Key and Value matrices, and advanced techniques such as PagedAttention further optimize cache memory management efficiency.
80%VerifiedvLLM is one of the most mainstream open-source inference serving frameworks, implementing efficient KV cache management through its PagedAttention technology.
75%VerifiedInference throughput optimization relies on technologies such as KV Cache management and Continuous Batching, which directly determine response speed and service cost under concurrent user loads.
75%VerifiedPagedAttention divides the KV Cache into fixed-size blocks, allocates them dynamically on demand, and allows blocks from different requests to be stored non-contiguously.
75%VerifiedThe KV Cache (Key-Value Cache) strategy stores already-computed attention key-value pairs in GPU memory for reuse, which consumes a large amount of GPU memory resources.
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