System Optimizations for LLM Inferencing

Fast LLM inferencing requires sufficient GPU memory to accommodate the entire model. However, many users find low-end GPUs inadequate or even unusable for this purpose. Our focus is on developing system methodologies and solutions to speed up LLM inferencing, such as offloading, data traffic optimization, neuron and weight redistribution, and token batching and rescheduling.

To address the challenges of LLM inferencing on low-end GPUs, we propose a series of system optimizations to enhance performance and efficiency. These optimizations include:

• Offloading: Delegating parts of the model computation to external systems or devices to alleviate GPU memory constraints.
• Data Traffic Optimization: Reducing latency and improving data transfer efficiency to accelerate inferencing.
• Neuron and Weight Redistribution: Balancing the computational load across different hardware components for better resource utilization.
• Token Batching and Rescheduling: Streamlining the processing flow to enhance performance and throughput.