On-device LLM inference increasingly relies on low-bit quantization, but practical formats such as W4A16 do not map cleanly to existing client NPU software stacks. We present TileFuse, a fused mixed-precision kernel library for AMD XDNA2 NPUs that directly consumes quantized weights, performs on-the-fly dequantization, and executes GEMM/GEMV without materializing BF16 weights in DRAM. TileFuse combines offline interleaved pre-tiling, optimized microkernels, and a memory-core-assisted 2D weight-distribution dataflow. Across Ryzen AI platforms, TileFuse achieves over 2× kernel-level gains and up to 1.9× faster prefilling with over 50% lower energy in end-to-end LLM inference in comparison with the iGPU baseline.

Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. However, engineering design poses a fundamentally different challenge for large language models (LLMs) compared with traditional textbook-style problem solving or factual question answering. Although existing benchmarks have driven progress in areas such as language understanding, code synthesis, and scientific problem solving, real-world engineering design demands the synthesis of domain knowledge, navigation of complex trade-offs, and management of the tedious processes that consume much of practicing engineers' time. Despite these shared challenges across engineering disciplines, no benchmark currently captures the unique demands of engineering design work. In this work, we introduce EngDesign, an Engineering Design benchmark that evaluates LLMs' abilities to perform practical design tasks across nine engineering domains. Unlike existing benchmarks that focus on factual recall or question answering, EngDesign uniquely emphasizes LLMs' ability to synthesize domain knowledge, reason under constraints, and generate functional, objective-oriented engineering designs. Each task in EngDesign represents a real-world engineering design problem, accompanied by a detailed task description specifying design goals, constraints, and performance requirements. EngDesign pioneers a simulation-based evaluation paradigm that moves beyond textbook knowledge to assess genuine engineering design capabilities and shifts evaluation from static answer checking to dynamic, simulation-driven functional verification, marking a crucial step toward realizing the vision of engineering Artificial General Intelligence (AGI).