A detailed hardware comparison for running large language models locally in 2026, covering Apple Silicon (M3 Pro/Max) versus NVIDIA GPUs (RTX 3090/4090). Key factors examined include memory capacity (unified memory vs VRAM), memory bandwidth, inference speed benchmarks, power consumption, and software ecosystems. The M3 Max 96GB is the only single-device consumer option for 70B models, while NVIDIA GPUs deliver 2-4x faster token generation for models that fit in 24GB VRAM. A budget-based recommendation matrix maps hardware choices to use cases ranging from casual experimentation to team serving, with notes on future-proofing for RTX 50-series and M4 chips.
Table of contents
Mac vs PC for Local LLMs ComparisonTable of ContentsWhy Run LLMs Locally in 2026?How Local LLMs Use HardwareApple Silicon for Local LLMs: M3 Pro and M3 MaxNVIDIA GPUs for Local LLMs: RTX 3090 and RTX 4090Head-to-Head Benchmarks: Mac vs PCSoftware Ecosystem: Ollama, vLLM, and BeyondHardware Recommendation Matrix by Budget and Use CaseFuture-Proofing Your SetupFinal Verdict: Mac or PC for Local LLMs?Sort: