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.
Proposing LLMS for efficient LLM context management and acceleration of mobile AI services.
Efficient memory management is crucial for the successful implementation of LLM as a system service on mobile devices.
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.