Architectural Innovation: From Single-Hardware Dependency to Heterogeneous Compatibility

A research team has validated the feasibility of fine-grained Expert Parallelism (EP) across two heterogeneous computing architectures, breaking traditional large models’ dependency on specific hardware ecosystems. The modular design decouples model execution from underlying hardware through three independent layers:

Heterogeneous Computing Validation

The team implemented dual-track technical routes for NVIDIA GPUs and domestic NPUs:

• CUDA Ecosystem: Optimized MegaMoE attention mechanism and DeepGEMM matrix operations enable real-time inference with 1.6T parameters per node
• NPU Architecture: Quantization-aware training and mixed-precision computing maintain 98% model accuracy at 16-bit floating-point precision

This dual-track design enables automatic computation backend switching based on deployment environments.

Hardware Decoupling Mechanism

The three-layer architecture (feature extraction, expert network, output aggregation) offers:

• Unified development: CUDA toolchain for algorithm validation
• Automatic deployment: Hardware-specific code generation
• Seamless maintenance: Online computation node switching without service interruption

Technical reports show 82% reduction in code modifications during cross-platform migration, lowering multi-cloud deployment barriers.

Inference Efficiency Breakthroughs: KV Cache Optimization and FLOPs Reduction

The new architecture achieves 73% FLOPs reduction per token and 90% KV cache memory savings in 1M-token contexts, addressing long-text processing challenges through two innovations:

Hierarchical KV Cache Mechanism

# Pseudocode for hierarchical caching
class HierarchicalKVCache:
    def __init__(self):
        self.global_cache = {}  # Stores global key features
        self.local_cache = {}   # Maintains local context
        self.sparse_matrix = SparseTensor()  # Sparsified attention matrix
    def update(self, new_tokens):
        important_features = select_important_features(new_tokens)
        self.global_cache.update(important_features)
        self.local_cache = update_sliding_window(self.local_cache, new_tokens)

This mechanism reduces memory usage from 48GB to 4.8GB for 1M tokens while maintaining 99.2% retrieval accuracy.

Dynamic Expert Activation

The architecture implements non-linear FLOPs reduction through:

• Base layer: All 49B expert parameters activated
• Deep processing: Dynamic selection of 3-5 experts (avg. 13B parameters)
• Output layer: Gated aggregation of expert outputs

Benchmark tests show FLOPs reduction from 1.2T to 320B per token in code generation tasks without performance degradation.

Performance Benchmarking: Redefined Evaluation Metrics

The architecture demonstrates industry-leading programming capabilities but shows room for improvement in knowledge processing tasks:

Programming Excellence

• LiveCodeBench Score: 93.5 vs. 91.6 for mainstream models
• Key improvements:
  • Formal verification module for code correctness
  • Enhanced recursion handling for deeper code generation
  • Dual-decoder architecture separating syntax and logic processing

Knowledge Processing Limitations

• SimpleQA-Verified Score: 57.9 (industry leaders at ~65)
• Optimization directions:
  • Multi-modal knowledge fusion enhancement
  • Dynamic knowledge graph updating
  • Improved entity disambiguation in long texts

Deployment Implementation Guide

The technical report provides detailed configuration recommendations and performance models:

Hardware Configuration Options

Scenario	Recommended Setup	Expected QPS
Real-time systems	8×A100 GPUs + 256GB RAM	120
Batch processing	32×V100 GPUs + 512GB RAM	850
Edge computing	2×Domestic NPUs + 64GB RAM	35

Cost Optimization Strategies

• FP16 Mixed Precision: 40% memory savings
• 8-bit Quantization: 2.3× speed increase with <1% accuracy loss
• Dynamic Batching: Maintains >85% GPU utilization

Future Development Directions

The research team plans three priority areas:

1. Multi-modal Fusion: Integrating visual and auditory data
2. Adaptive Computing: Dynamic resource allocation based on input complexity
3. Federated Learning: Distributed privacy-preserving computation

Hardware ecosystem collaboration will focus on:

• New memory architecture optimization
• Optical interconnect integration
• Co-design with compute-in-memory chips

Conclusion

This breakthrough represents a paradigm shift in large model development—from parameter scaling to efficiency optimization, from hardware dependency to cross-platform compatibility. Developers gain cost-effective deployment options while enterprises unlock multi-cloud strategies and edge computing possibilities. With upcoming availability of domestic computing hardware, we anticipate order-of-magnitude reductions in model deployment costs, accelerating AI democratization.