Analysis of DeepSeek large model parameter storage technology

Parameter sparse storage technology is one of the core innovations of DeepSeek's large model to achieve efficient computing and resource optimization. Its core concept is to reduce redundant computing and improve resource utilization by dynamically allocating and selectively activating model parameters. This technology is based on the hybrid expert architecture (MoE) and Transformer deep optimization, combined with dynamic routing, load balancing and other mechanisms, to form a complete parameter management paradigm.

1.1 Dynamic parameter activation of hybrid expert architecture (MoE)

DeepSeek uses the MoE architecture to achieve physical dispersion and logical centralization of parameters. In the model structure, each feedforward network layer is replaced by a MoE layer composed of multiple independent expert sub-networks. For example, DeepSeek-V3 contains 256 routing experts and 1 shared expert, and each input token activates only 8 experts (about 5.5% of the total parameters). This design allows a model with a total of 671 billion parameters to only activate 37 billion parameters in a single inference, significantly reducing computational complexity.

Key technical breakthroughs include:

Sparse activation mechanism: Dynamically select relevant experts through the gating network to avoid all parameters from being involved in the calculation. The gating network uses a low-rank attention mechanism to optimize the accuracy of routing decisions and ensure the semantic relevance of expert selection.

Load balancing without auxiliary loss: Traditional MoE architecture needs to introduce auxiliary loss function to balance the expert load, but it will lead to performance degradation. DeepSeek uses dynamic redundancy strategy to achieve expert load balancing without relying on additional loss terms, which improves the efficiency of computing resource allocation by 40%.

1.2 Deep Optimization of Transformer Architecture

Based on Transformer, DeepSeek introduces two key improvements:

1. Multi-head Latent Attention (MLA): Through low-rank joint compression technology, the dimension of the Key-Value matrix is ​​reduced from O(n²) to O(n), reducing the KV cache occupancy. For example, when processing 128K long text, the MLA mechanism reduces the memory requirement to 1/3 of the traditional attention mechanism while maintaining the semantic association accuracy.

2. Dynamic sequence segmentation : Automatically divide the input sequence according to hardware characteristics, and combine the FlashAttention algorithm to optimize GPU memory bandwidth utilization, reducing attention calculation latency by 30%.

2.1 Dynamic Routing and Computing Resource Allocation

The dynamic routing network is the core execution layer of parameter storage, and its workflow is divided into three stages:

1. Input feature analysis: Use lightweight convolutional networks to extract features such as complexity and semantic type of input content. For example, it can identify formula structure and logical operator distribution when dealing with mathematical problems.

2. Resource demand prediction: Based on the feature analysis results, predict the computational load of different neural network modules (such as attention head, expert sub-network) and generate a resource allocation heat map.

3. Real-time scheduling decision: Dynamically adjust the computing path based on hardware status (such as GPU memory margin and bandwidth utilization). In long text processing scenarios, the system will allocate 80% of computing resources to the MLA module, giving priority to ensuring contextual coherence.

2.2 Model Compression and Quantization Technology

To achieve efficient storage and transmission of parameters, DeepSeek adopts a multi-level compression strategy:

Structured pruning: Remove redundant experts in the MoE layer through importance scoring algorithms (such as gradient amplitude analysis). Experiments show that pruning inactive experts can reduce the model size by 15% and increase the inference speed by 22%.

Hybrid precision quantization: FP8 precision (activation value group quantization + weight block quantization) is used in the training phase, which saves 50% of video memory compared to FP16 precision; INT8 dynamic quantization is supported in the deployment phase, allowing 70B parameter models to run on mobile devices.

Knowledge distillation: Through the teacher-student framework, the capabilities of the 670B parameter model are transferred to a 7B small model, achieving parameter level compression while maintaining 90% performance.

2.3 Distributed training and inference optimization

DeepSeek's distributed system design achieves physical dispersion and logical unification of parameters:

1. Training phase: Using a four-dimensional parallel strategy (data parallelism, pipeline parallelism, tensor parallelism, and expert parallelism), we achieved 2788K GPU hours of ultra-large-scale training on a cluster of 2048 H800 GPUs. The expert parallel technology distributes the MoE layer on 64 computing nodes, and uses the DualPipe algorithm to overlap communication and computing, which improves training efficiency by 37%.

2. Inference phase: The deployment solution adopts a prefill and decode separation architecture. In the prefill phase, 4 nodes with 128 GPUs are used to process prompts, and in the decoding phase, 40 nodes with 320 GPUs are used for autoregressive generation. The throughput reaches 1500 tokens/s through dynamic

batch processing technology .

3.1 Practical application effect

Improved computing efficiency: In the financial risk prediction task, DeepSeek-Pro (13B parameters) reduces inference latency by 50% and energy consumption by 63% compared with dense models of the same size.

Multimodal support: Cross-modal attention sharing is achieved through parameter distribution, and the accuracy of joint image and text reasoning tasks is improved by 28%, while the memory usage only increases by 12%.

Edge deployment capability: DeepSeek-Lite (1B parameters) quantized by INT8 can achieve real-time conversation on the mobile phone, with a response time of less than 500ms.

3.2 Technical Challenges and Solutions

1. Long context modeling: When processing text with more than 100K tokens, dynamic routing decision errors may cause semantic faults. The solution includes the introduction of explicit memory units and hierarchical attention mechanisms, which have improved information completeness to 92% in the 128K text summary task.

2. Load balancing jitter: Expert load fluctuations may cause computing resources to be idle. By introducing a sliding window load prediction algorithm, the standard deviation of resource utilization was reduced from 15.7% to 4.2%.

3. Multimodal alignment bias: Parameter dispersion during joint training of images and text may weaken modal associations. The contrastive learning loss function is used to strengthen the cross-modal attention weights, and the alignment accuracy is improved to 89% in the VQA task.

1. Hardware co-design: Developing dedicated AI chips to support dynamic parameter loading is expected to increase the energy efficiency of the MoE architecture by another 3 times.

2. Self-evolving system: By automatically synthesizing training data to optimize parameter distribution, it has achieved a 40% improvement in zero-sample generalization capabilities in code generation tasks.

3. Green computing practice: The goal is to run a 10B parameter model at 1W power consumption, and the current prototype has achieved a 70% energy efficiency target.

DeepSeek's parameter storage technology marks a paradigm shift in large model design from "scale first" to "efficiency first". Through the deep integration of architectural innovation and system engineering optimization, this technology provides a reusable technical blueprint for the popularization of AI, and its evolution direction will continue to promote artificial intelligence from laboratory research to large-scale industrial implementation.