Platform Development Context and Technical Positioning

Driven by rapid advancements in AI large models and growing data security concerns, the Blockchain and Data Security National Key Laboratory collaborated with a university research excellence center to launch the DeepSeek Local Deployment Platform in March 2025. This solution specifically addresses academic research requirements by establishing a trustworthy environment spanning data preprocessing to model inference using domestic technology stacks.

Core Positioning Dimensions:

1. Technological Autonomy: Achieved national independence through Ascend processor-based deployment of the DeepSeek-R1 671B model
2. Research Adaptability: Integrated AI agent technology to optimize model interactions for experimental scenarios
3. Security Control: Implemented full lifecycle data protection to meet research data sensitivity requirements

Key Advantages Over Cloud Solutions:

• Data sovereignty through local processing
• Dedicated computing clusters using domestic hardware
• Customizable model training pipelines

Domestic Technology Architecture

The platform establishes a closed-loop ecosystem through three-tier collaboration between chips, frameworks, and algorithms:

1. Hardware Infrastructure

• Distributed computing clusters built on Ascend 910B AI processors with 3D packaging for high-speed interconnect
• All-flash storage system achieving microsecond-level latency via RDMA networking
• Support for thousand-billion parameter model training in single clusters

2. System Software Layer

Developed a domestic hardware-optimized deep learning framework featuring:

# Framework optimization example: Mixed precision training
class AutoMixedPrecision:
    def __init__(self, model):
        self.fp16_groups = []
        self.fp32_groups = []
    def apply(self):
        for name, param in model.named_parameters():
            if 'weight' in name or 'bias' in name:
                self.fp16_groups.append(param)
            else:
                self.fp32_groups.append(param)
        # Dynamic precision switching logic...

• Dynamic memory management through pooling techniques
• Gradient compression to reduce distributed training overhead
• Deterministic execution for experimental reproducibility

3. Algorithm Toolkits

Provides three specialized research packages:

• Multimodal Processing: Supports joint modeling of text, images, and point clouds
• Causal Inference: Built-in counterfactual reasoning algorithms
• Few-shot Learning: Optimized for data-scarce research scenarios

Three Core Technical Innovations

1. Hardware-Software Co-Optimization

Developed processing-near-memory (PNM) architecture for large models:

• Memory-compute fusion through HBM-integrated compute units
• Data locality optimization via intelligent caching
• 40% improvement in training energy efficiency

Key components:

1. Data partitioning engine for optimal memory allocation
2. Dynamic workload scheduler balancing compute and bandwidth
3. Real-time thermal management system

2. AI Security Evaluation Framework

Implemented dynamic red-blue team security assessment:

graph TD
    A[Original Model] --> B{Security Evaluation}
    B -->|Attack Detection| C[Red Team Simulation]
    B -->|Defense Assessment| D[Blue Team Validation]
    C --> E[Vulnerability Repair]
    D --> F[Robustness Enhancement]
    E --> G[Safety Alignment Training]
    F --> G
    G --> H[Security-Enhanced Model]

• Simulates 132 known attack patterns
• Reinforcement learning with human value constraints
• Continuous behavior monitoring system

3. Content Safety Protection

Multi-layer defense system comprising:

• Input Filtering: NLP-based sensitive information detection
• Generation Control: Dynamic decoding strategy adjustment
• Output Auditing: Multimodal compliance verification

Enhanced through adversarial training:

# Adversarial training implementation
def adversarial_train(model, dataloader, adversary):
    optimizer = torch.optim.Adam(model.parameters())
    for inputs, labels in dataloader:
        adv_inputs = adversary.perturb(inputs, labels)
        outputs = model(torch.cat([inputs, adv_inputs]))
        loss = criterion(outputs, labels.repeat(2))
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

Academic Application Cases

1. Biomedical Research

• Maintained data confidentiality for protein structure prediction
• Enabled continuous 7×24 training with dedicated resources
• Integrated domain-specific knowledge graphs

2. Materials Science

• Provided multi-scale modeling toolchains
• Supported high-performance molecular dynamics simulation
• Achieved 60% reduction in material discovery cycles

3. Social Sciences

• Covered 37 academic languages in text analysis
• Included ethical review module for bias detection
• Generated explanation chains for research conclusions

Technology Evolution and Ecosystem

The development team is pursuing three innovation directions:

1. Heterogeneous computing support for domestic GPUs/DPUs
2. Automated model performance tuning systems
3. Cross-disciplinary knowledge integration frameworks

Ecosystem initiatives include:

• Partial toolchain source code release
• Annual domestic AI technology summit
• University consortium network establishment

This “technology innovation + ecosystem collaboration” model establishes a replicable paradigm for domestic AI research infrastructure development, marking significant progress in China’s AI foundational research capabilities.