MoE-CAP Evaluation Framework

• MoE-CAP Evaluation Framework is a benchmarking tool that defines and measures trade-offs among cost, accuracy, and performance in sparse Mixture-of-Experts systems.
• It introduces the CAP Radar Diagram and sparsity-aware metrics to provide a visual and quantitative assessment of system efficiency in heterogeneous hardware settings.
• The framework guides hardware provisioning and system design by clarifying how improvements in one dimension necessitate trade-offs in others.

The MoE-CAP Evaluation Framework is designed to benchmark and analyze the deployment and efficiency of sparse Mixture-of-Experts (MoE) systems, particularly for large-scale models such as those encountered in modern language and vision applications. MoE-CAP establishes a principled approach for evaluating trade-offs among three core dimensions: Cost, Accuracy, and Performance (“CAP”). By introducing the CAP Radar Diagram and sparsity-aware metrics, this framework addresses the unique challenges posed by MoE architectures, including resource allocation in heterogeneous hardware settings and the sparse activation of model parameters.

1. Foundational Principles and Motivation

The framework originates from the observation that conventional benchmarks for dense neural networks fail to account for the distinctive behavior of sparse MoE models, whose efficiency depends on how tokens are routed to expert subnetworks under varying hardware and cost constraints. MoE-CAP formalizes the multi-dimensional relationship among system cost (comprising hardware acquisition and energy expenditure), accuracy (as measured by standard downstream tasks), and performance (including throughput, latency, and achieved hardware utilization). It characterizes the essential trade-off: typically, an MoE system may optimize two of these three aspects, requiring compromise on the third. This is referred to as the “MoE-CAP trade-off” (Jiang et al., 10 Dec 2024, Jiang et al., 16 May 2025).

2. Definitions: CAP Trade-off and System Classes

The core construct of MoE-CAP is the formalization of the CAP trade-off:

• Cost (C): Encompasses hardware price and operational energy requirements.
• Accuracy (A): Standard task performance metrics (e.g., within LLMs: exact match, perplexity, pass@k, etc.).
• Performance (P): Encompasses throughput [tokens/sec], latency, and resource utilization metrics (e.g., memory bandwidth, FLOPS).

MoE systems are classified into three design types:

System Class	Optimized Dimensions	Example Trade-off
PA	Performance + Accuracy	High-end hardware, high validation accuracy, high throughput; elevated costs.
CP	Cost + Performance	Resource-constrained deployments, reduced accuracy via quantization/low-rank or model pruning.
CA	Cost + Accuracy	Low-cost endpoints, preserve accuracy by sacrificing throughput (latency increases).

This framework posits that optimizing all three metrics simultaneously is infeasible with current architectural and hardware constraints; improvement in one aspect necessitates sacrifices in at least one of the others (Jiang et al., 10 Dec 2024, Jiang et al., 16 May 2025).

3. CAP Radar Diagram and Visualization Approach

The CAP Radar Diagram is a multi-axis visualization: each axis corresponds to one CAP metric, and a system configuration is plotted according to its measured values in each dimension. The resulting shape provides a rapid assessment of “trade-off balance.” For instance, a configuration with maximal cost-efficiency but weak accuracy will display a truncated profile on the accuracy axis. This diagram is a diagnostic and comparative tool, supporting decisions for system designers on resource investment and configuration tuning (Jiang et al., 10 Dec 2024, Jiang et al., 16 May 2025).

4. Sparsity-Aware Performance Metrics

Traditional resource benchmarks overestimate compute/memory requirements for MoE systems by assuming all parameters are active. MoE-CAP introduces sparsity-aware metrics:

• Sparse Memory Bandwidth Utilization (S-MBU):

$$S\text{-}MBU = \frac{B_{\text{achieved}}}{B_{\text{peak}}}$$

where $$B_{\text{achieved}} = (S_{\text{activated}} + S_{KV}) / TPOT$$, and $$S_{\text{activated}} = \sum_{l=1}^{n_\text{layer}} \sum_{i=1}^{n_\text{expert}} \mathbb{1}[l, i] \times S_\text{expert}$$, counting only the memory accessed for router-selected experts (with $TPOT$ tokens per batch).

• Sparse Model FLOPS Utilization (S-MFU):

$$S\text{-}MFU = \frac{T_{\text{token}} \times S\text{-}F_{\text{token}}}{F_{\text{peak}}}$$

where $$S\text{-}F_{\text{token}} = F_\text{attn} + 2N_\text{router} + 2k_\text{expert} N_\text{expert}$$, incorporating only the FLOPS of activated experts plus router computations.

These metrics help determine true hardware requirements by preventing resource overallocation and mismatched provisioning, particularly relevant when deploying on heterogeneous devices or scaling batch sizes (Jiang et al., 10 Dec 2024, Jiang et al., 16 May 2025).

5. Hardware Deployment and Resource Considerations

MoE-CAP is applicable to varied hardware settings:

• High-end GPU (e.g., A100, H100): Supports high-accuracy/high-performance deployments at elevated cost, optimal when low latency and maximal throughput are essential.
• HHeterogeneous or Consumer Devices: Enables cost-efficient deployments by relying on high model sparsity, especially effective under small batch-size (e.g., inference at batch=1).
• Communication and Memory Offload: CAP trade-offs become especially meaningful as batch size increases (activating more experts), higher FFN dimension or more active experts per token increase peak resource demands.

Resource allocation decisions are optimized by evaluating achieved S-MBU/S-MFU and visualizing via the CAP Radar Diagram, ensuring efficient use of available hardware (Jiang et al., 10 Dec 2024, Jiang et al., 16 May 2025).

6. Practical Implications for System Design and Benchmarking

MoE-CAP provides actionable guidance:

• End-to-end Benchmarking: Measures system efficiency and accuracy in context of true hardware cost (including energy), not just raw throughput or validation loss.
• Resource Provisioning Decisions: Enables rational selection of hardware (e.g., less expensive GPU where S-MBU/S-MFU is sufficiently high for target accuracy).
• Configurational Tuning: Supports optimization across use cases—production, personal device inference, or research training—by reflecting trade-offs in the CAP Radar Diagram.
• Prevents Over-provisioning: Accurate bandwidth and compute estimation avoids unnecessarily expensive deployments.

The framework supports co-design, allowing architecture choices (e.g., number of experts, router sparsity, quantization) matched to application requirements.

7. Future Directions and Extensions

Initial work indicates further potential avenues:

• Expanded Cloud and Serverless Support: Adapting CAP-based benchmarking for elastic, spot, or serverless deployments to handle variable cost/performance trade-offs.
• Emerging Hardware Evaluation: Benchmarks may be extended to account for new accelerators and device classes.
• Cost Model Refinement: As new deployment strategies emerge, models can be enriched to better account for dynamic resource pricing and energy consumption.

Ongoing work may also include more sophisticated benchmarking tools and integration with real-world deployment managers.

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The MoE-CAP Evaluation Framework establishes a comprehensive, multidimensional approach for benchmarking sparse Mixture-of-Experts systems, reconciling the unique demands of model sparsity, heterogeneous deployments, and resource-aware accuracy management. Through the CAP Radar Diagram and sparsity-aware utilization metrics, it provides a principled basis for comparative evaluation and informed decision-making in large-scale AI system design (Jiang et al., 10 Dec 2024, Jiang et al., 16 May 2025).