LLM Inference and GPU Limitations

LLMs, such as the Transformer architecture, consist of multiple layers that process input sequences to generate outputs or predictions. The inference process is memory-intensive, as it requires the storage of a complete set of model parameters and intermediate activation states. For mid-range GPUs with limited memory, this poses a significant challenge. The memory capacity of these GPUs restricts the size of the LLM that can be deployed and the batch size that can be processed simultaneously, leading to underutilization of computational resources and increased latency.