InferScale: GPU-Native KV Injection for Personalized LLM Serving
Score
81提出 GPU 原生 KV 注入的完整系统设计，解决了端侧内存注入的延迟痛点，创新性较高。
论文结构清晰，实验设计合理，对比基线充分，但仅在一个数据集（LoCoMo）上验证。
基于 vLLM 实现，无需修改引擎即可部署，对高并发、长记忆的 LLM 服务有直接降本增效作用。
论文于 2026 年 7 月发布，紧贴当前个性化 LLM 服务的热点需求，时效性极佳。
来源为 arXiv 预印本，作者单位可信，且公开了实现代码（基于 vLLM），信源可靠性高。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
The paper presents a clear system design, thorough experimental validation, and an open-source implementation based on vLLM, offering strong reproducibility.
AI commentary
「This paper stands out by moving KV cache reuse from system scheduling to GPU memory level, directly addressing the pain point where longer memories lead to slower first tokens in personalized services. Engineering teams building chatbots, virtual assistants, or knowledge-based Q&A systems should try this immediately—especially vLLM users can integrate with near-zero cost. The slight accuracy drop is well worth the 3-4x throughput gain.」
Quick overview
InferScale introduces a GPU-native KV injection system that eliminates repeated prompt prefilling in personalized LLM serving. It precomputes KV representations of memory facts, stores them with semantic embeddings on GPU, and injects them directly into vLLM’s paged cache at serving time. With Chunked RoPE and Context-Window Encoding to handle position encoding and cross-fact context loss, InferScale keeps TTFT nearly constant as retrieval budget grows, reducing TTFT by 72-79% at k=50, boosting throughput 3.7-4.5x, and achieving accuracy close to Mem0 baseline (60.3% vs 63.3%) without serving-time recomputation.
- InferScale reduces first-token latency in personalized LLM serving by replacing repeated prefilling with precomputed KV caches.
- Introduces Chunked RoPE and Context-Window Encoding to handle position encoding and missing cross-fact context during dynamic memory assembly.
- Implemented via vLLM's KV-connector interface without engine modifications or model fine-tuning.
- On LoCoMo benchmark, at k=50, TTFT is reduced by 72-79%, concurrent throughput improved by 3.7-4.5x, with only a 3 percentage point accuracy drop vs Mem0.
Full text
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