Compute-Accuracy Trade-offs in Open-Source LLMs
Published:Dec 31, 2025 10:51
•1 min read
•ArXiv
Analysis
This paper addresses a crucial aspect often overlooked in LLM research: the computational cost of achieving high accuracy, especially in reasoning tasks. It moves beyond simply reporting accuracy scores and provides a practical perspective relevant to real-world applications by analyzing the Pareto frontiers of different LLMs. The identification of MoE architectures as efficient and the observation of diminishing returns on compute are particularly valuable insights.
Key Takeaways
- •Evaluates open-source LLMs considering both accuracy and computational cost.
- •Identifies Mixture of Experts (MoE) architecture as a strong candidate for balancing performance and efficiency.
- •Highlights a saturation point where increased compute yields diminishing accuracy gains.
Reference
“The paper demonstrates that there is a saturation point for inference-time compute. Beyond a certain threshold, accuracy gains diminish.”