In the global artificial intelligence (AI) race, the US's ChatGPT (OpenAI) and Gemini (Google) hold leading positions, while China's DeepSeek is emerging as a new force, leveraging local advantages and technological iterations. This article compares the three in terms of technological performance, application scenarios, and infrastructure support, and analyzes the core role of high-speed optical modules in their development.
Technological Architecture and Performance
ChatGPT (GPT-4): Based on the Transformer architecture and trained on vast multilingual datasets, its strengths lie in natural language generation (NLG) and complex logical reasoning. It excels in creative writing, code generation, and multi-turn conversations but has limitations in accuracy within Chinese contexts and real-time information updates.
Gemini: As a representative of multimodal models, Gemini integrates text, image, and video processing capabilities, particularly excelling in cross-modal retrieval (e.g., "text from image") and search engine synergy (leveraging Google Knowledge Graph). However, its high computational demands restrict lightweight deployment.
DeepSeek: Focused on optimizing for Chinese scenarios, it excels in classical literature understanding, dialect recognition, and local compliance (e.g., adherence to China's data security laws). Its model parameters are smaller, with higher training efficiency, but it lags behind the other two in multimodal support and global corpus coverage.
Application Scenarios and Commercialization
ChatGPT and Gemini are widely embedded in global markets such as office, education, and customer service, while DeepSeek specializes in vertical fields like finance and government in China, providing customized solutions. For instance, DeepSeek can automatically relate financial report analysis to Chinese market regulatory policies, while ChatGPT is more adept at international standardization tasks.
Computational Power and Cost Efficiency
Gemini relies on Google’s in-house TPU clusters, incurring the highest training costs; ChatGPT utilizes Microsoft Azure’s GPU supercomputing, requiring substantial hardware investment; DeepSeek employs a mixed computational strategy (domestic chips + international hardware) to seek a balance between cost control and domestic substitution.
High-speed optical modules are core components for achieving high-speed data transmission within data centers, transmitting data via optical signals over fiber optics, with bandwidths exceeding 800Gbps and latencies below microseconds. Their key roles in AI development are reflected in:
Multiplier for Training Efficiency
Distributed training of large models requires frequent synchronization of vast parameters (e.g., GPT-4's 1.8 trillion parameters). Insufficient communication speed between nodes can lead to idle computing resources. For example, OpenAI revealed that high-speed optical modules reduced training cycles by 40%, while the OCS (optical circuit switching) technology deployed by Google for Gemini further optimized the utilization efficiency of optical modules.
Cornerstone for Real-Time Inference
In AI applications (e.g., ChatGPT's conversational responses), user requests must reach the model and return results within milliseconds. High-speed optical modules ensure low-latency communication within data centers and across geographic nodes, especially in financial transaction scenarios served by DeepSeek, where a 0.1-second latency difference can significantly impact decision value.
Challenges and Breakthroughs in Chinese Technology
Chinese optical module companies (e.g., Zhongji Xuchuang, Guangxun Technology) have captured over 40% of the global market share but still lag behind US manufacturers (e.g., Coherent, Intel) in the 800G/1.6T ultra-high-speed module field. For DeepSeek to catch up with trillion-parameter-level models, it must rely on breakthroughs in domestic optical modules regarding power consumption and speed.
ChatGPT: Continues to expand its advantages in multimodality and generality but faces strict compliance reviews in Europe and the US.
Gemini: Leverages the Google ecosystem to strengthen search-AI integration but must address computational cost challenges.
DeepSeek: Adopts a "small but refined" strategy to cultivate the domestic market while accelerating the localization of foundational technologies like optical modules to support the training of larger models.
The AI competition is not only a contest of algorithms but also a game of infrastructure. High-speed optical modules, as the "blood vessels of data," will directly impact the iteration speed of models and the ceiling of applications. If China’s DeepSeek can achieve a closed loop in its core technology chain, it may carve out a unique path in the global AI landscape.
In the global artificial intelligence (AI) race, the US's ChatGPT (OpenAI) and Gemini (Google) hold leading positions, while China's DeepSeek is emerging as a new force, leveraging local advantages and technological iterations. This article compares the three in terms of technological performance, application scenarios, and infrastructure support, and analyzes the core role of high-speed optical modules in their development.
Technological Architecture and Performance
ChatGPT (GPT-4): Based on the Transformer architecture and trained on vast multilingual datasets, its strengths lie in natural language generation (NLG) and complex logical reasoning. It excels in creative writing, code generation, and multi-turn conversations but has limitations in accuracy within Chinese contexts and real-time information updates.
Gemini: As a representative of multimodal models, Gemini integrates text, image, and video processing capabilities, particularly excelling in cross-modal retrieval (e.g., "text from image") and search engine synergy (leveraging Google Knowledge Graph). However, its high computational demands restrict lightweight deployment.
DeepSeek: Focused on optimizing for Chinese scenarios, it excels in classical literature understanding, dialect recognition, and local compliance (e.g., adherence to China's data security laws). Its model parameters are smaller, with higher training efficiency, but it lags behind the other two in multimodal support and global corpus coverage.
Application Scenarios and Commercialization
ChatGPT and Gemini are widely embedded in global markets such as office, education, and customer service, while DeepSeek specializes in vertical fields like finance and government in China, providing customized solutions. For instance, DeepSeek can automatically relate financial report analysis to Chinese market regulatory policies, while ChatGPT is more adept at international standardization tasks.
Computational Power and Cost Efficiency
Gemini relies on Google’s in-house TPU clusters, incurring the highest training costs; ChatGPT utilizes Microsoft Azure’s GPU supercomputing, requiring substantial hardware investment; DeepSeek employs a mixed computational strategy (domestic chips + international hardware) to seek a balance between cost control and domestic substitution.
High-speed optical modules are core components for achieving high-speed data transmission within data centers, transmitting data via optical signals over fiber optics, with bandwidths exceeding 800Gbps and latencies below microseconds. Their key roles in AI development are reflected in:
Multiplier for Training Efficiency
Distributed training of large models requires frequent synchronization of vast parameters (e.g., GPT-4's 1.8 trillion parameters). Insufficient communication speed between nodes can lead to idle computing resources. For example, OpenAI revealed that high-speed optical modules reduced training cycles by 40%, while the OCS (optical circuit switching) technology deployed by Google for Gemini further optimized the utilization efficiency of optical modules.
Cornerstone for Real-Time Inference
In AI applications (e.g., ChatGPT's conversational responses), user requests must reach the model and return results within milliseconds. High-speed optical modules ensure low-latency communication within data centers and across geographic nodes, especially in financial transaction scenarios served by DeepSeek, where a 0.1-second latency difference can significantly impact decision value.
Challenges and Breakthroughs in Chinese Technology
Chinese optical module companies (e.g., Zhongji Xuchuang, Guangxun Technology) have captured over 40% of the global market share but still lag behind US manufacturers (e.g., Coherent, Intel) in the 800G/1.6T ultra-high-speed module field. For DeepSeek to catch up with trillion-parameter-level models, it must rely on breakthroughs in domestic optical modules regarding power consumption and speed.
ChatGPT: Continues to expand its advantages in multimodality and generality but faces strict compliance reviews in Europe and the US.
Gemini: Leverages the Google ecosystem to strengthen search-AI integration but must address computational cost challenges.
DeepSeek: Adopts a "small but refined" strategy to cultivate the domestic market while accelerating the localization of foundational technologies like optical modules to support the training of larger models.
The AI competition is not only a contest of algorithms but also a game of infrastructure. High-speed optical modules, as the "blood vessels of data," will directly impact the iteration speed of models and the ceiling of applications. If China’s DeepSeek can achieve a closed loop in its core technology chain, it may carve out a unique path in the global AI landscape.
