LoRA (Low-Rank Adaptation)
A parameter-efficient fine-tuning technique that adds small trainable matrices to a frozen pre-trained model, enabling customization with a fraction of the compute and memory normally required.
LoRA, introduced by Microsoft researchers in 2021, typically trains only 0.1-1% of a model's parameters while achieving performance close to full fine-tuning. This reduces GPU memory requirements by up to 3x and training costs by 10-100x. LoRA has become the most popular method for customizing open-source LLMs like Llama and Mistral. Variants like QLoRA combine LoRA with quantization to fine-tune 65-billion-parameter models on a single 48GB GPU — a task that would otherwise require multiple high-end GPUs.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Compute
The computational resources — primarily GPU and TPU processing power — required to train and run AI models, typically measured in FLOP (floating-point operations) or GPU-hours.
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Data Center
A facility housing computer systems and infrastructure used to process, store, and distribute data — increasingly built specifically for AI training and inference workloads.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
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