Hy4: Balancing Performance and Cost in Large Language Models Through Sparse Activation Architecture

1. Evolution of MoE Architecture: From Parameter Stacking to Precision Activation

Throughout the development of large language models, there has been a direct correlation between parameter scale and inference cost. According to a 2025 industry report by a cloud provider, activated parameters account for over 78% of the inference cost in mainstream models with hundreds of billions of parameters. The Mixture of Experts (MoE) architecture addresses this challenge through sparse activation, enabling models to scale to trillions of parameters while activating only a subset of expert networks during each inference.

Hy4’s 78-layer network architecture exemplifies MoE design excellence. Beyond the initial fully connected layer, each of the remaining 77 layers incorporates 256 routing experts and one shared expert. This configuration transforms the inference process for each input token into an expert selection problem. Through dynamic routing, the model activates the top eight routing experts and the shared expert from a pool of 257, ensuring that only 49 billion parameters (6.36% of the total 770 billion) are engaged per inference.

Compared to traditional dense models with nearly 100% parameter utilization, Hy4 reduces effective computational density to 6.36% through architectural innovation. This sparsity translates to significant cost savings: terminal testing reveals an 82% reduction in inference energy consumption and a 76% decrease in memory usage compared to dense models, laying the groundwork for scalable deployments.

2. Three Pillars of Sparsity: Architecture, Attention, and Caching Optimization

Hy4’s sparse activation strategy rests on three core innovations:

Dynamic Routing for Expert Selection
Each token undergoes a two-stage routing process: a gating network computes weights for all experts, followed by activation of the top eight routing experts. This approach eliminates redundant computations in fully connected layers, with routing accounting for just 12% of total inference time in experiments.

Gated Dynamic Sparse Attention (G-DSA)
Traditional attention mechanisms have O(n²) time complexity. Hy4 introduces G-DSA, which employs dynamic masking to focus attention computations on critical tokens. Testing with 1 million-token contexts demonstrated a 68% reduction in attention computations, with only a 1.2% decline in task accuracy.

IndexCache for Long-Text Optimization
For long-text scenarios, Hy4 adopts a hierarchical caching strategy. Historical context is encoded into index vectors stored in high-speed cache, with new tokens prioritizing cached data during inference. This reduces inference latency for 1 million-token texts from 12.7 seconds to 3.2 seconds, achieving a 91% cache hit rate.

3. Performance-Cost Tradeoffs: Comparative Analysis of Flagship Models

A comparison of Hy4 with two leading models highlights distinct technical approaches:

Model	Total Parameters	Activated Parameters	Activation Ratio	Sparsity Strategy	Cost Advantage Index*
Hy4	770B	49B	6.36%	Dynamic routing + attention optimization + caching acceleration	1.00
GLM 5.3 Flash	320B	18B	5.63%	Aggressive expert pruning	1.15
Qwen3.8-Flash-Next	125B	6B	4.8%	N-gram embedding table memory offloading	1.32

*Cost Advantage Index: Based on inference cost for equivalent tasks; higher values indicate lower cost per unit of performance.

GLM 5.3 Flash: Aggressive Sparsity
By reducing routing experts from 256 to 128, GLM 5.3 Flash achieves a 5.63% activation ratio. However, this leads to a 3.7% accuracy drop in complex reasoning tasks, illustrating a clear performance-sparsity tradeoff.

Qwen3.8-Flash-Next: Extreme Optimization
This model offloads a 51 billion-parameter embedding table to host memory, loading it only when needed. While reducing the core model to 125B parameters, this approach demands high-speed memory buses, imposing stricter deployment requirements.

Hy4: The Balanced Approach
With a 6.36% activation ratio, Hy4 achieves equilibrium between cost and performance. It scored 85.4 on the Terminal Bench 2.1 programming test (matching Claude Opus 5) and earned a 2.99/4 rating in blind evaluations by 163 experts (slightly outperforming GLM 5.3). This pragmatic design suits enterprise applications.

4. Developer Perspectives: Practical Value of Sparse Architectures

Hy4’s architecture offers developers three key benefits:

Cost Predictability
Priced at $0.000006 per input token and $0.000018 per output token, Hy4 makes deploying models with hundreds of billions of parameters feasible for SMEs. A smart customer service provider reported reducing daily inference costs from $23,000 to $8,000 after migrating to Hy4.

Performance Consistency
Unlike aggressively sparse models, Hy4 maintains stability in long-text processing (e.g., legal document analysis) and complex reasoning (e.g., code generation). Its dynamic routing prevents expert overload, keeping inference quality fluctuations below 2%.

Engineering Friendliness
Hy4 provides a complete model compression toolkit, supporting dynamic parameter pruning from 770B to 49B. Developers can adjust activated expert counts to linearly scale performance and cost between 0.5B and 49B parameters.

5. Future Directions: The Evolution of Sparse Architectures

As MoE technology matures, next-generation large models will exhibit three trends:

Heterogeneous Expert Networks
Combining CPU/GPU/NPU capabilities, models will feature experts with varying computational precision—e.g., INT8 for general text and FP16 for mathematical computations.

Adaptive Sparsity
Mechanisms that dynamically adjust activation ratios based on task complexity could reduce inference costs by an additional 40%, according to preliminary experiments.

Federated Expert Learning
Privacy-preserving approaches will enable multiple models to share expert networks without exchanging raw data. One research team achieved a 5.3% accuracy improvement through collaborative training of cross-institutional expert networks.

As computational costs continue to rise, Hy4’s sparse activation architecture provides a vital reference for the industry: architectural innovation, not mere parameter expansion, can drive model performance breakthroughs. This philosophy of “computing smarter, not harder” may define the next era of large model development.