The key-value (KV) cache in transformer-based language models stores intermediate computations (keys and values) for all previous tokens, enabling efficient autoregressive decoding. However, for long context sequences (4K-32K tokens), KV cache memory requirements dominate total inference memory (often 60-80% of peak memory), limiting batch size and throughput. This study presents a sliding window KV-cache mechanism combined with importance scoring to reduce memory requirements while maintaining generation quality. The approach maintains only the most recent N tokens (sliding window) in the KV cache, discarding older tokens as new ones are generated. We introduce adaptive importance scoring based on attention weights: tokens with high cumulative attention in recent generation steps are retained in cache, while low-importance tokens are discarded. We evaluate on multiple architectures (Llama 2-7B, Mistral 7B, LLaMA-13B) and tasks (long-document summarization, retrieval-augmented generation, long-context question answering). With a 2048-token sliding window covering 2048/4096 = 50% of a 4K context: Perplexity remains within 2-3% of full-context baseline (typically 93-98% recovery), Memory savings reach 45-55% reduction in KV cache size, Throughput improves 1.8-2.1x due to reduced memory bandwidth, Latency per token decreases by 35-42%. For extreme compression (512-token window covering 12.5% of 4K context): Quality degrades more significantly (80-85% perplexity recovery), but memory reduction reaches 75-80%, enabling batch size improvements of 3-4x. The importance scoring mechanism uses recent attention patterns to identify which older tokens remain relevant. Validation shows the method preserves long-range dependencies needed for retrieval-augmented tasks (retrieval precision within 1-2% of full context). This framework enables efficient inference on memory-constrained devices while maintaining reasonable quality for most applications.