Researchers from the University of British Columbia and Microsoft Research have introduced SeKV, a new inference optimization technique that significantly reduces the memory overhead of large language models processing long-context inputs. The method, co-developed by Yuhang He, a senior researcher at Microsoft Research and Oxford University doctoral graduate, tackles a persistent scalability issue in AI: the linear expansion of key-value cache requirements as document length grows. Conventional caching mechanisms demand dozens of gigabytes of GPU VRAM for texts exceeding 100,000 tokens, frequently overwhelming consumer hardware and crippling inference speed. SeKV employs a hybrid retrieval architecture that separates high-frequency access from complete cache storage. Rather than holding every token key-value pair in GPU memory, the system partitions input text into semantic segments and compresses their detailed KV matrices via singular value decomposition. Only condensed summaries and strategic semantic anchors reside in VRAM for rapid indexing, while full-resolution data remains in CPU memory. When contextual relevance is detected, the model dynamically fetches and reconstructs the required segments. This design prevents the permanent data loss typical of aggressive cache pruning or static summarization approaches. A lightweight routing module, containing less than 0.05 percent of the base model parameters, drives the system. Training requires only two to six hours across eight NVIDIA A100 GPUs, and the optimized module integrates directly into existing architectures without full fine-tuning. In deployment, SeKV reduces VRAM consumption by more than 53 percent at 128K context lengths. Benchmarks indicate that functioning at one-tenth of the original memory footprint improves response accuracy by nearly six percentage points relative to established semantic compression methods. As a result, standard 24GB consumer GPUs can now comfortably handle contexts nearing 300K tokens. By removing the reliance on expensive enterprise-grade hardware, SeKV provides a practical pathway for high-context AI adoption. The technology is expected to accelerate routine processing in legal contract review, academic paper synthesis, and long-form narrative analysis, offering organizations a memory-efficient, cost-effective solution for scaling LLM deployments to enterprise document volumes.