Visual Language Model

58 Tech

Apr 11, 2025 · Artificial Intelligence

Optimization of Multimodal Visual Large Model Inference: Pre‑processing, ViT TensorRT, CUDA Graphs, Tokenization, Prefix Cache, and Quantization

This report details a comprehensive set of optimizations for multimodal visual large‑model (VLM) inference—including image pre‑processing acceleration, TensorRT integration for the ViT module, CUDA‑Graph replay, token‑count reduction, prefix‑cache handling, and weight quantization—demonstrating up to three‑fold throughput gains while maintaining accuracy.

CUDA GraphTensorRTVisual Language Model

0 likes · 19 min read

Optimization of Multimodal Visual Large Model Inference: Pre‑processing, ViT TensorRT, CUDA Graphs, Tokenization, Prefix Cache, and Quantization

DataFunSummit

Feb 21, 2025 · Artificial Intelligence

Multimodal Retrieval‑Augmented Generation (RAG): Implementation Paths and Future Prospects

This article explores multimodal Retrieval‑Augmented Generation (RAG), detailing five core topics—including semantic extraction, visual‑language models, scaling strategies, technical roadmap choices, and a Q&A—while presenting three implementation pathways, performance evaluations, and future directions for AI‑driven document understanding.

Document UnderstandingRAGTensor Retrieval

0 likes · 11 min read

Multimodal Retrieval‑Augmented Generation (RAG): Implementation Paths and Future Prospects

Sohu Tech Products

Jan 8, 2025 · Artificial Intelligence

Multimodal RAG: Implementation Paths and Development Prospects

The talk outlines Multimodal RAG implementation routes, comparing OCR‑based object recognition, transformer encoder‑decoder encoding, and Visual Language Model processing, explains the ColPali late‑interaction method for multi‑dimensional vector matching, addresses scaling tensors with binarization and reranking, and recommends a hybrid long‑term strategy where VLM excels on abstract imagery while traditional OCR remains valuable.

ColPaliMultimodal RAGOCR

0 likes · 10 min read

Multimodal RAG: Implementation Paths and Development Prospects