Local LoRA Fine-Tuning VRAM: How Much GPU Memory You Need for QLoRA and LoRA
Local LoRA fine-tuning VRAM explained: Unsloth minimums for QLoRA vs LoRA from 3B to 70B, what raises memory use, and the levers that fix training OOM.
Local LoRA fine-tuning VRAM explained: Unsloth minimums for QLoRA vs LoRA from 3B to 70B, what raises memory use, and the levers that fix training OOM.
MLX vs llama.cpp on Mac: how mlx-lm and GGUF tools differ on Apple silicon, install steps, model formats, memory limits and which one to choose.
本地微调显存需求详解:Unsloth 文档给出的 3B 到 70B 的 QLoRA 与 LoRA 显存下限、哪些因素会推高占用,以及训练爆显存时的调整手段。
MLX 和 llama.cpp 在 Mac 上有何区别:mlx-lm 与 GGUF 工具的安装、模型格式、内存限制,以及怎样选择。
How to run LLMs on a Mac: what 16GB to 128GB of unified memory can hold, usable GPU share, bandwidth by chip, and Mac vs GPU advice.
Mac 跑大模型要多少统一内存?按 16GB 到 128GB 对照可运行的模型、可用显存比例和带宽,并对比 Mac 与独显、给出购买建议。