Activated Parameters
Activated parameters refer to the subset of a model’s total parameters that are actively utilized during inference for a specific input, as opposed to the model’s total parameter count. This concept is critical in Mixture-of-Experts (MoE) architectures, where only a fraction of parameters are dynamically activated per token, optimizing computational efficiency without sacrificing performance.
Key Examples
- Kimi K2 (Moonshot AI’s MoE model) utilizes 32 billion activated parameters out of a total 1 trillion parameters, achieving state-of-the-art performance in knowledge-intensive tasks.
- DeepSeek V4: A next-gen, open-source suite of LLMs optimized for high performance and architectural efficiency.
- Benchmarking against agents like gemini, chatgpt, Grok DeepSearch, and Manus confirmed its superior research capabilities.
Related Concepts
References
- 2026 04 24 DeepSeek V4 Next Gen Open Source LLM Performance and Efficiency Analysis
Source Notes
- 2026-04-14: # Kiki K2 - Prompt Engineering --- --- https://www.youtube.com/watch?v=lDXc-zVqN1w The video provides a detailed overview of Moonshot AI’s Kimi K2 model and its research capabilities, then compares va (Kiki K2 - Prompt Engineering)