大模型轻量化推理加速方案
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案例介绍
Built a complete optimization pipeline to solve the problem of high memory consumption and slow inference of deep learning models. Adopted knowledge distillation, quantization, and pruning for model compression. Combined CUDA and torch.compile for inference acceleration. Created automated evaluation scripts to compare latency, memory usage, and accuracy before and after optimization, lowering hardware deployment costs for edge-side model deployment.
大模型轻量化推理加速方案
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案例介绍
Built a complete optimization pipeline to solve the problem of high memory consumption and slow inference of deep learning models. Adopted knowledge distillation, quantization, and pruning for model compression. Combined CUDA and torch.compile for inference acceleration. Created automated evaluation scripts to compare latency, memory usage, and accuracy before and after optimization, lowering hardware deployment costs for edge-side model deployment.
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