This paper presents KVShare, a multi-user Key-Value (KV) Cache sharing technology based on semantic similarity, designed to enhance the inference efficiency of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). Addressing the limitations of existing prefix caching (strict text prefix matching) and semantic caching (loss of response diversity), KVShare achieves fine-grained KV cache reuse through semantic alignment algorithms and differential editing operations. Experiments on real-world user conversation datasets demonstrate that KVShare improves KV cache hit rates by over 60%, while maintaining output quality comparable to full computation (no significant degradation in BLEU and Rouge-L metrics). This approach effectively reduces GPU resource consumption and is applicable to scenarios with repetitive queries, such as healthcare and education.

Web applications are increasingly becoming the primary platform for AI service delivery, making in-browser deep learning (DL) inference more prominent. However, current in-browser inference systems fail to effectively utilize advanced web programming techniques and customize kernels for various client devices, leading to suboptimal performance. To address the issues, this paper presents the first in-browser inference system, nn-JIT.web, which enables just-in-time (JIT) auto-generation of optimized kernels for both CPUs and GPUs during inference. The system achieves this by using two novel web programming techniques that can significantly reduce kernel generation time, compared to other tensor compilers such as TVM, while maintaining or even improving performance. The first technique, Tensor-Web Compiling Co-Design, lowers compiling costs by unifying tensor and web compiling and eliminating redundant and ineffective compiling passes. The second technique, Web-Specific Lite Kernel Optimization Space Design, reduces kernel tuning costs by focusing on web programming requirements and efficient hardware resource utilization, limiting the optimization space to only dozens. nn-JIT.web is evaluated for modern transformer models on a range of client devices, including the mainstream CPUs and GPUs from ARM, Intel, AMD and Nvidia. Results show that nn-JIT.web can achieve up to 8.2x faster within 30 seconds compared to the baselines across various models.