Scaling Large Models on Limited Hardware: The KV Cache Compression Solution

The article reveals the exact formula for calculating KV cache memory consumption in LLM inference, showing that this overhead often exceeds model weights themselves—a critical bottleneck for long-context processing. Understanding KV cache requirements is essential for production LLM systems, as it directly determines whether inference requires single or multiple GPUs. The NexusQuant compression technique demonstrated here transforms the feasibility equation, converting multi-GPU necessities into single-GPU deployments and making large-scale inference practically accessible.