Hy4: Achieving Cost-Performance Equilibrium in Large Language Models Through Mixture of Experts Architecture

1. Technological Breakthrough: Sparse Activation Revolution via MoE Architecture
   Hy4 employs a MoE architecture with 770 billion parameters, dynamically activating only 49 billion (6.36%) per inference—a leading ratio in the industry. This “on-demand” sparse computing model operates through a three-tier routing mechanism:

1.1 Hierarchical Routing Design
The model’s 78-layer network allocates 256 routing experts and 1 shared expert per layer (except the first). For each input token, the system selects Top-8 routing experts via dynamic routing algorithms, forming a computational matrix with the shared expert. This design reduces FLOPs per inference to 1/16th of traditional dense models.

1.2 Sparse Attention Optimization
The Gated Dynamic Sparse Attention (DSA) mechanism uses gating units to dynamically adjust attention weight distributions. When processing 1M-length contexts, it reduces attention computation from O(n²) to O(n log n), achieving 90% key-value pair cache reuse through IndexCache technology.

1.3 Parameter Efficiency Equilibrium
Experimental data shows accuracy declines significantly in code generation tasks when activated parameters drop below 40 billion. Hy4 selects 49 billion as its equilibrium point, scoring 85.4 on Terminal Bench 2.1—matching top-tier models—while maintaining cost advantages of ¥6/million input tokens and ¥18/million output tokens.

1. Architectural Innovation: Engineering Breakthroughs Beyond Parameter Scale
   Hy4 achieves qualitative sparsity improvements through three core technologies:

2.1 Dynamic Routing Algorithm Optimization
A two-stage routing strategy combines hash-based pre-filtering (64 candidate experts) with soft attention weighting. This reduces routing decision time from 12ms to 3.2ms while maintaining 98.7% routing accuracy.

2.2 Expert Load Balancing Mechanism
The Gradient Balancing Loss function dynamically adjusts expert selection probabilities, reducing token processing standard deviation among experts to 12% (vs. 35% in traditional MoE models). This prevents performance fluctuations caused by expert overload.

2.3 Heterogeneous Computing Adaptation
A sharding deployment strategy divides 256 routing experts into 16 computational groups, with each group’s 16 experts sharing a single GPU core. This increases per-card expert capacity from 8 to 256, boosting hardware utilization by 300%.

1. Industry Comparison: Divergent Paths in Sparsity Strategies
   Comparative analysis of three mainstream sparse models reveals technical route differentiation:

3.1 Aggressive Sparsification
A cloud provider’s 125 billion-parameter model achieves 4.8% activation by offloading 510 billion N-gram embeddings to memory, trading space for time. However, this requires dedicated servers with ≥1TB memory, increasing deployment costs by 45%.

3.2 Balanced Design
Another platform’s 320 billion-parameter model uses 5.63% activation with reinforcement learning-enhanced routing. While performance improves by 3%, training consumes 20% additional computational resources, violating sustainability principles.

3.3 Robust Approach
Hy4’s 6.36% activation appears conservative but delivers comprehensive advantages:

• Training stability: Expert activation fluctuations controlled within ±5%
• Inference latency: 99th percentile delay <200ms
• Scalability: Supports linear expansion to trillion-parameter scale

1. Enterprise Deployment: Critical Considerations for Practical Applications
   Hy4 offers three core values for enterprise users:

4.1 Cost Optimization Framework
In a financial institution’s intelligent customer service scenario, dynamic batching reduced cost per QPS from ¥0.12 to ¥0.03. Combined with model quantization, GPU memory usage decreased by 60%, enabling 70 billion-parameter model deployment on 8-card A100 clusters.

4.2 Performance Tuning Toolkit
A visual routing heatmap tool helps developers identify inefficient experts. In a code generation platform application, adjusting expert allocation improved Java code generation accuracy by 8.2 percentage points.

4.3 Security Isolation Mechanism
Expert-level sandboxing runs different experts in isolated process spaces. When malicious inputs are detected, problematic experts can be quickly isolated without disrupting overall service, achieving 99.99% availability in government platform tests.

1. Future Trajectory: Evolutionary Directions for Sparsity Technologies
   Three key trends will shape sparsity technology development:

5.1 Dynamic Sparsity Adjustment
Research focuses on adaptive mechanisms that automatically adjust activated parameter counts based on input complexity—reducing to 30 billion for simple Q&A and increasing to 80 billion for complex reasoning.

5.2 Expert Specialization Training
Domain-specific expert libraries will be trained for different business scenarios, such as contract parsing experts for legal applications and medical record comprehension experts for healthcare. Preliminary tests at a top-tier hospital showed 15% higher diagnostic accuracy with specialized experts.

5.3 Hardware-Software Co-Design
Collaboration with chip manufacturers aims to develop dedicated accelerators supporting sparse computing. Through hardware-level routing optimization and zero-value compression, inference energy efficiency could improve fivefold compared to current levels.

Conclusion: As the model scale competition shifts to efficiency competition, Hy4 demonstrates that architectural innovation-driven sparsity delivers more sustainable value than mere parameter expansion. For enterprises, selecting sparsity solutions aligned with specific business scenarios proves more critical than chasing the latest model parameter counts. With maturing technologies like dynamic routing and expert specialization, large models are entering an era of truly intelligent “on-demand computing.”