The KV Cache Compression Race: TurboQuant vs OSCAR vs EpiCache

Recent technical developments, including TurboQuant, OSCAR, and EpiCache, are addressing the growing memory bottleneck caused by the key-value cache in large language models. As models process longer context windows, the memory required to store this cache can exceed the size of the model weights themselves. These compression methods aim to optimize memory usage during inference, potentially allowing models to handle more extensive data without requiring additional hardware resources.