Fine-tuning of Large Language Models (LLMs) using Low-Rank Adaptation (LoRA) on an end-user’s data offers personalized experiences while keeping data private, but faces severe memory constraints on consumer hardware. Peak memory during fine-tuning often exceeds device limits, especially for models with billions of parameters and long-context training data. This paper introduces a suite of complementary techniques to reduce memory footprint: (1) base model quantization with on-the-fly dequantization, (2) memory-efficient checkpointing combining selective activation caching and disk offloading, (3) softmax approximation using semantically relevant token subsets, and (4) logits masking. Only softmax approximation alters the training objective and we discuss the resulting quality trade-off both analytically and empirically. Experiments on Llama-3.2 3B and Qwen-2.5 3B demonstrate up to 26× and 28× reduction in peak memory, enabling fine-tuning on resource-constrained devices.