Intelligence Density
The metric representing the ratio of model capability and reasoning performance to parameter count or computational footprint. Maximizing this ratio is a central goal in model-efficiency and efficient inference.
Core Principles
- Information Per Bit: Maximizing semantic utility and reasoning accuracy within reduced-precision weights.
- Structural Efficiency: Utilizing advanced architectures like model-efficiency to compress large language models (LLMs) while minimizing the degradation of intelligence.
Recent Developments
- bonsai-8b (developed by prismml): An implementation of a 1-bit LLM based on the Qwen 2.0 8B architecture, achieving high intelligence density.
- Demystifying AI: Transformer Training on a 1979 PDP-11
- Author / channel: Dave’s Garage
- URL: https://www.youtube.com/watch?v=OUE3FSIk46g
- Summary: Demonstrates the training process of a neural network using a transformer on a vintage 1979 44 computer with single CPU and limited memory, highlighting the core principles without relying on modern GPU clusters.
2026 04 13 Demystifying AI Transformer Training on a 1979 PDP 11
Source Notes
- 2026-04-07: PrismML Bonsai 8B First Look & Test - A TRUE 1-Bit LLM?
- 2026-04-22: Google Gemma · ▶ source