Breaking Memory Barriers: 33x KV Cache Compression Without Retraining

The KV cache bottleneck severely limits LLM deployment at long contexts—128K tokens can require 60+ GB of memory, consuming even high-end GPUs entirely. NexusQuant eliminates this constraint through intelligent token eviction and quantization, enabling production-scale inference without expensive model retraining or calibration data. This breakthrough removes a critical barrier to deploying powerful language models with extended context windows on consumer and mid-range hardware.