The article explains that unified memory on Apple Silicon determines the maximum LLM size you can run locally, provides a memory-tier breakdown from 8GB to 192GB+, recommends tools like Ollama and LM Studio, and warns about quantization, context window, and CPU offloading pitfalls.
This article explains how to estimate the GPU memory required for running large language models of 3 B, 7 B, and 13 B parameters, walks through step‑by‑step calculations, shows how hardware limits affect feasibility, and offers practical optimization techniques such as quantization and CPU offloading.
This article introduces Pai‑Megatron‑Patch, a suite of tools built on Nvidia Megatron‑LM that accelerates large language model training through dense and MoE model support, high‑precision HuggingFace↔MCore weight conversion, CPU offloading for optimizers and activations, FlashAttention‑3, and communication‑compute overlapping, and provides detailed experimental results and command‑line usage examples.
