🚀 The rise of large language models (#LLMs) and machine intelligence applications comes with significant concerns about the substantial resource requirements of these technologies. The demand for expensive hardware and high energy consumption has sparked creative solutions—one of which is #quantization.

• Quantization reduces the size of large language models (LLMs) by lowering parameter precision (e.g., from 32-bit to 8-bit or 4-bit).
• Benefits:
  • Smaller storage requirements enable deployment on devices like smartphones.
  • Improved energy efficiency and lower computational costs.
• Techniques:
  • Post-training quantization (applied after model training).
  • Quantization-aware training (incorporates quantization during training for better accuracy).
• Challenges: Potential accuracy loss and increased implementation complexity.
• Advances: Research on 1-bit models and methods to avoid matrix multiplications to enhance efficiency.

Quantization is a encouraging approach to making LLMs more accessible and sustainable.

For an insightful explanation of this approach (German, paywall): www.heise.de/hintergru…