High-speed large language model locally deployed inference engine
PowerInfer is an engine for high-speed large language model inference on PCs using consumer GPUs. It exploits the high locality feature in LLM inference by preloading thermally activated neurons onto the GPU, thereby significantly reducing GPU memory requirements and CPU-GPU data transfer. PowerInfer also integrates adaptive predictors and neuron-aware sparsity operators to optimize the efficiency of neuron activation and computational sparsity. It can perform inference on a single NVIDIA RTX 4090 GPU at an average generation rate of 13.20 tokens per second, which is only 18% lower than the top server-grade A100 GPU. while maintaining model accuracy.
PowerInfer is suitable for high-speed inference of large language models deployed locally.
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MouSi is a multi-modal visual language model designed to address current challenges faced by large-scale visual language models (VLMs). It uses integrated expert technology to collaborate the capabilities of individual visual encoders, including image-text matching, OCR, image segmentation, etc. This model introduces a fusion network to uniformly process outputs from different vision experts and bridge the gap between image encoders and pre-trained LLMs. In addition, MouSi also explored different position encoding schemes to effectively solve the problems of position encoding waste and length limitation. Experimental results show that VLMs with multiple experts exhibit superior performance than isolated visual encoders, and obtain significant performance improvements as more experts are integrated.
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