Unverified50% confidenceFactExact time
In actual inference, VLMs have significantly higher VRAM usage and first-token latency than pure text models, so deployment often employs methods such as dynamic resolution, visual token compression, or cache reuse.
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50%
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Long-term
Relevance
10/15/2026
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UnverifiedFor VLMs, the visual encoding stage concentrates heavy computation during the prefill phase, while the language decoding stage is limited by memory bandwidth.74% similarUnverified微调VLM能够复用大模型已有的视觉理解与语言生成能力，在相对有限的标注数据下实现对特定文献类型的适配73% similarUnverifiedvLLM通过PagedAttention机制解决了KV Cache的显存碎片化问题，在大语言模型推理场景下实现了远超传统方案的吞吐量70% similarUnverifiedvLLM has recently introduced features such as prefix caching and speculative decoding to reduce time-to-first-token latency and improve response efficiency.70% similarVerifiedA VLM consists of three components: a visual encoder, a language model backbone, and a cross-modal alignment module.66% similar
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