Efficient Deployment of Large Language Models on Mobile Devices
To enable high-efficiency deployment of large language models (LLMs) on mobile device GPUs, the paper proposes four key optimization techniques: (1) a symbolic expression-based approach for dynamic shape model inference, (2) operator optimizations and execution priority setting, (3) an FP4 quantization method to reduce dequantization overhead, and (4) a sub-tensor-based technique to eliminate the need for copying KV cache after LLM inference.
LLM as a System Service on Mobile Devices: Paradigm Shift in Mobile AI
Proposing LLMS for efficient LLM context management and acceleration of mobile AI services.
LLM as a System Service on Mobile Devices: Context Management for Efficient Memory Usage
Efficient memory management is crucial for the successful implementation of LLM as a system service on mobile devices.
Revolutionizing Mobile AI with M4: A Comprehensive Analysis
The author argues that the M4 foundation model can revolutionize mobile AI by providing a unified, adaptable, and multimodal approach to handling diverse tasks efficiently and effectively.
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