InferScale: GPU-Native KV Injection for Personalized LLM Serving

InferScale is a GPU-native LLM memory system that replaces repeated prompt prefilling with reusable KV state, addressing the TTFT increase caused by injecting persistent personalized context into prompts. It precomputes KV representations for memory facts, stores them with semantic embeddings on the GPU, and injects them directly into vLLM's paged cache, using Chunked RoPE for dynamic memory assembly and Context-Window Encoding to mitigate the loss of cross-fact context. On LoCoMo with three open-weight models, InferScale keeps TTFT nearly constant as retrieval budget grows, reducing TTFT by 72-79% (3.6-4.8x) at k=50, achieving 60.3% accuracy versus 63.3% for Mem0, and delivering 3.7-4.5x throughput under concurrent load. This decouples memory-conditioned serving latency from retrieved-context size while preserving application quality, enabling efficient personalized LLM serving without engine modifications or fine-tuning.