Token‑Size‑Aware Compression Reduces LLM Memory Footprint

As large language models (LLMs) grow in complexity, their memory demands have become a critical bottleneck. Modern models with hundreds of billions of parameters require extreme computational resources to store and process token data during inference. For example, a single long-context generation…