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 GraphTensorRTinference-optimization

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

DataFunSummit

Nov 4, 2024 · Artificial Intelligence

Performance Optimization Techniques for Large Model Inference Frameworks

This article outlines four key optimization areas for large model inference frameworks—quantization, speculative sampling, TTFT/TPOT improvements, and communication optimization—detailing specific techniques, experimental results, and practical benefits such as reduced memory usage, lower latency, and higher throughput.

AISpeculative Samplinginference-optimization

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Performance Optimization Techniques for Large Model Inference Frameworks