Model Architecture
Core Concept
Model Architecture refers to the structural design and organization of components within an Artificial Intelligence system, particularly Large Language Models (LLMs). It defines how data flows, how parameters are organized, and how computational resources are utilized to generate outputs.
Evolution: Harness vs. Model
Recent developments indicate a paradigm shift where the surrounding infrastructure (“harness”) is becoming as critical as the underlying model weights.
- The Harness Shift: The “harness” (prompt engineering, orchestration, tool use, and context management) is increasingly determining performance outcomes more than the base model’s raw capabilities Beyond Transformers: Exploring State-Space and Recurrent AI Model Architectures.
Emerging Architectures: Beyond Transformers
While Transformers have dominated since 2017 (underpinning models like ChatGPT, Claude, and Gemini), inherent weaknesses are driving research into alternative structures Beyond Transformers: Exploring State-Space and Recurrent AI Model Architectures.
- Quadratic Complexity: Transformers suffer from quadratic computational costs relative to sequence length, limiting efficiency for long-context tasks.
- State-Space Models (SSMs): Emerging architectures like Mamba offer linear scaling for sequence processing, potentially addressing the efficiency bottlenecks of attention mechanisms.
- Recurrent Architectures: Modern recurrent models are being revisited for their ability to maintain state with lower computational overhead compared to self-attention layers.