Optimize LLM inference latency for edge devices

Large language models are increasingly being deployed in edge environments including smartphones, IoT devices, and embedded systems. However, current inference times are too slow for real-time applications. We're looking for innovative solutions that can: - Reduce inference latency by at least 50% - Maintain model quality within 5% of the baseline - Work across different hardware architectures (ARM, RISC-V) - Be compatible with popular frameworks (ONNX, TensorFlow Lite) Solutions can include model compression, quantization, architectural innovations, or novel inference engines.