The Convergence of MoE Models: Why 10 Billion Active Parameters?

In the rapidly evolving landscape of Large Language Models (LLM), Mixture of Experts (MoE) models have emerged as a promising alternative to traditional dense models, offering significant efficiency potential. A recent interesting observation points to an unexpected convergence: despite widely varying total sizes, a significant number of MoE models tend to activate approximately 10 billion parameters during inference.

This pattern has been noted across various implementations. For instance, the Qwen 3.5 model, despite having a total size of 122 billion parameters, activates only 10 billion. Similarly, MiniMax M2.7, a model with 230 billion total parameters, uses a “Top 2” routing mechanism to also activate around 10 billion parameters. This trend raises questions about the underlying reasons for such a threshold.

The Economics of Training: A Driving Factor

The primary reason behind this convergence appears to lie in the economic dynamics of model training. The training cost of an MoE model is approximated by the formula C ≈ 6 × N_active × T, where N_active represents the number of active parameters and T is the number of tokens used for training. This relationship highlights how cost is directly proportional to the parameters actually utilized, not the total model size.

Considering a model with 10 billion active parameters and a training dataset of 15 trillion tokens, the computational cost is around 9e23 FLOPs. This value is significantly lower than that required to train a dense 70 billion parameter model on an equivalent amount of data, estimated at approximately one-seventh of the cost. This economic disparity effectively pushes development teams to optimize their MoE models towards this active parameter threshold to contain development costs.

Inference Challenges and Memory Management

While MoE models offer advantages in terms of training costs, they present specific challenges for inference, particularly regarding memory management. An open question in the industry concerns the scaling of inference memory when the number of “experts” (the specialists within the MoE model) increases, but the number of active parameters remains constant.

This issue is crucial for infrastructure designers, as the amount of VRAM available on GPUs is a primary constraint. Regardless of the expert configuration, it appears that the KV cache (Key-Value cache) tends to dominate memory requirements once the context window exceeds 32,000 tokens. This means that, even with a fixed number of active parameters, long contexts can still demand a considerable amount of memory, influencing hardware selection and throughput capacity.

Implications for Deployment Strategies

For CTOs, DevOps leads, and infrastructure architects evaluating LLM deployment, understanding these dynamics is fundamental. The choice between MoE and dense models, and the optimization of active parameters, directly impacts the Total Cost of Ownership (TCO) of self-hosted or hybrid solutions. The ability to perform inference efficiently, with manageable VRAM requirements and adequate throughput, is a critical factor.

Organizations prioritizing data sovereignty, compliance, or the need for air-gapped environments, and thus opting for on-premise deployments, must carefully consider how model architecture influences hardware selection (e.g., GPUs with 80GB VRAM or more), latency, and scalability. AI-RADAR offers analytical frameworks on /llm-onpremise to support the evaluation of these trade-offs, providing tools to compare the constraints and opportunities of different LLM architectures in local deployment contexts.