The operational landscape of local Large Language Model (LLM) inference has shifted from lightweight models to datacenter-class weights exceeding 70B parameters, creating profound systems challenges for consumer hardware. This paper presents a systematic empirical analysis of the Nvidia and Apple Silicon ecosystems, specifically characterizing the distinct intra-architecture trade-offs required to deploy these massive models. On the Nvidia Blackwell architecture, we identify a critical "Backend Dichotomy" within the TensorRT-LLM stack: while the new NVFP4 quantization format delivers a 1.6x throughput advantage over optimized BF16 baselines (151 tokens/s vs. 92 tokens/s), realizing this performance requires navigating complex runtime constraints that trade startup latency for generation speed. Furthermore, we characterize the "VRAM Wall" for 70B+ models: on discrete GPUs, users face a destructive choice between aggressive quantization (e.g., Q2) that degrades model intelligence to fit in VRAM, or PCIe-bottlenecked CPU offloading, which reduces throughput by over 90% compared to full-GPU execution. Conversely, Apple's Unified Memory Architecture (UMA) circumvents these bottlenecks, enabling linear scaling for 80B parameter models at practical 4-bit precisions. This architectural divergence extends to operational sustainability, where Apple's SoC design demonstrates up to a 23x advantage in energy efficiency (tokens/joule). We conclude that for consumer-grade inference, the optimal hardware is defined by a complex interplay between compute density (Nvidia) and memory capacity (Apple), moderated by the significant "ecosystem friction" of proprietary quantization workflows.

翻译：本地大语言模型推理的运行格局已从轻量级模型转向参数量超过700亿的数据中心级权重，这给消费级硬件带来了深刻的系统挑战。本文对英伟达与苹果硅生态进行了系统性的实证分析，重点刻画了部署这些大规模模型所需的独特架构内部权衡。在英伟达Blackwell架构上，我们识别出TensorRT-LLM栈中的关键“后端分裂性”：新的NVFP4量化格式相比优化后的BF16基线，吞吐量优势达1.6倍（151 tokens/s vs. 92 tokens/s），但实现这一性能需应对复杂的运行时约束——这些约束以牺牲启动延迟换取生成速度。此外，我们刻画了70B+模型的“显存墙”现象：在独立GPU上，用户面临两种破坏性选择——要么采用激进量化（如Q2）导致模型智能退化以适配显存，要么采用受PCIe瓶颈限制的CPU卸载，其吞吐量相较全GPU执行降低90%以上。反之，苹果的统一内存架构（UMA）规避了这些瓶颈，使80B参数模型在实用的4位精度下实现线性扩展。这种架构差异延伸至运行可持续性：苹果片上系统（SoC）设计在能效（tokens/焦耳）上展现出高达23倍的优势。我们得出结论：对于消费级推理而言，最优硬件由计算密度（英伟达）与内存容量（苹果）的复杂相互作用决定，而专有量化工作流带来的显著“生态摩擦”则调节了这一平衡。