DeepSeek core architecture and key technologies innovation

DeepSeek core architecture and key technologies have been innovated. DeepSeek has achieved improved processing speed and reduced computing complexity through a series of key technological innovations, providing strong support for applications in related fields.

Edited at 2025-02-05 21:36:44

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DeepSeek core architecture and key technologies innovation

Gavinnblake

Recent works View more works>>

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Outline

DeepSeek Core architecture and key technologies innovation

Key technologies innovation

Efficient reasoning engine

FlashAttention optimization

Take advantage of the GPU memory bandwidth advantage to accelerate attention calculation and achieve delay reduction of more than 30%.

Dynamic batch processing technology

Flexible adjustment of batch size according to request complexity and optimize throughput.

Multimodal expansion

Unified representation space

Through CLIP-style comparison learning, accurate alignment of text, image and video embedded vectors is achieved, and cross-modal retrieval and generation are supported.

Multimodal Reasoning Engine

Integrate visual Transformer (ViT) and language models to empower cutting-edge applications such as graphic and text Q&A and video description generation.

Resource efficiency promote

Parameter efficient fine-tuning (PEFT)

Using LoRA technology, you can quickly adapt to new tasks by training only 1% parameters, and save up to 90% on video memory.

Quantification and distillation technology

Supports INT8 quantization and model distillation, so that the 10B-level model can run smoothly on edge devices (such as mobile phones).

Core architecture

Model cornerstone

Deeply optimize the Transformer architecture, integrate the sparse attention mechanism, and greatly reduce the computational complexity.

Introduce a dynamic routing network, intelligently allocate computing resources based on the input content, significantly improving the processing speed of long text and complex logical tasks.

Hierarchical strategy optimization

Hybrid expert system (MoE)

Built-in multiple expert subnets, activated on demand through a fine gating mechanism, enhance model capacity while maintaining controllable computing costs.

Phase training Essence

Pre-training stage

Immerse yourself in a trillion-level multilingual corpus (covering Chinese, English and code), and integrate knowledge graphs to deepen entity understanding.

Align stage

Combining human feedback reinforcement learning (RLHF) with constitutional AI concepts, ensure that the output is both safe and in line with value orientation.

Field fine adjustment stage

Inject professional data in specific fields such as finance and medical care to improve the performance of the model in professional tasks.