Unverified50% confidenceFactExact time
vLLM was developed by the University of California, Berkeley, and is known for its PagedAttention technology, which efficiently manages GPU memory and is suitable for high-concurrency production environments.
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50%
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Long-term
Relevance
9/1/2026
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UnverifiedvLLM通过PagedAttention技术将GPU显存利用率提升数倍，是开源社区最主流的高并发大模型推理引擎之一76% similarUnverifiedInference engines such as vLLM and TensorRT-LLM improve GPU throughput through techniques like PagedAttention and continuous batching.74% similarVerifiedvLLM's PagedAttention technology improves GPU memory utilization from the 20–40% of traditional approaches to close to 100%.69% similarUnverifiedRepresentative works in vision-language models (VLMs) include OpenAI's GPT-4V, Google's Gemini, and the open-source community's LLaVA.59% similarVerifiedVLMs (Vision Language Models) typically consist of a visual encoder (such as ViT), a language model, and a projection layer connecting the two, with representative models including GPT-4V/4o, Claude 3.5, the LLaVA series, and InternVL58% similar
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