Inference Engine

Inference engines are software systems designed to efficiently execute machine learning models, particularly large language models (LLMs), optimizing for speed, memory usage, and accuracy. Current research emphasizes improving inference speed through techniques like model quantization (reducing the precision of model parameters), optimized hardware acceleration (e.g., using specialized kernels and FPGAs), and novel architectural approaches such as sparse attention and early exits. These advancements are crucial for deploying LLMs in resource-constrained environments (like embedded systems and mobile devices) and for enabling real-time applications requiring high throughput and low latency.