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QSFP Transceiver Module

SFP Transceiver Module

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100G QSFP28 Transceiver

400G Transceiver

800G Transceiver

25G SFP28 Transceiver

40G QSFP+ Transceiver

Copper Transceiver

SGMII Transceiver

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OSA Components

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About Us
China Sichuan Trixon Communication Technology Corp.,Ltd
Sichuan Trixon Communication Technology Corp.,Ltd
Trixon is a leading manufacturer of optical transceiver module. It designs, develops, manufactures and markets high performance 155M~800G modules.
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01
HIGH QUALITY
Trust Seal, Credit Check, RoSH and Supplier Capability Assessment. company has strictly quality control system and professional test lab.
02
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Internal professional design team and advanced machinery workshop. We can cooperate to develop the products you need.
03
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Advanced automatic machines, strictly process control system. We can manufacture all the Electrical terminals beyond your demand.
04
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Bulk and customized small packaging, FOB, CIF, DDU and DDP. Let us help you find the best solution for all your concerns.
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Latest company news about U.S. Global Tariffs Reshape World Optical Module Market Amid AI Boom
2025-04-14

U.S. Global Tariffs Reshape World Optical Module Market Amid AI Boom

    The U.S. government’s 2025 tariff adjustments have triggered a seismic shift in the global optical module market, with far-reaching implications for supply chains, pricing, and technological innovation. While the tariffs aim to protect domestic industries, they have inadvertently exposed vulnerabilities in the highly interdependent semiconductor ecosystem, particularly as demand surges for AI-driven data centers and 5G infrastructure. Tariff Exemptions and Supply Chain Realignment Under the new policies, optical modules categorized under HS code 8517.62—critical components for high-speed data transmission—are eligible for tariff exemptions if they contain at least 20% U.S.-sourced components, such as Broadcom’s DSP chips or Lumentum’s lasers. This provision has created a dual-tiered market: Chinese manufacturers like Accelink Technologies and Innolight can bypass tariffs by using American-made parts, while smaller firms lacking such integration face steep costs. For instance, Accelink’s 800G modules, which rely on Broadcom’s chips for 22% of their content, could see gross margins rise by 3-5 percentage points post-exemption.   However, this dependency on U.S. technology has forced Chinese companies to accelerate domestic R&D. Innolight has invested $300 million in UL-certified facilities to reclassify its CPO modules under tariff-exempt HS codes, while Eoptolink plans to quadruple its Thailand-based production capacity to 1.8 million units annually by 2025 to mitigate tariff risks. Meanwhile, U.S. firms like Coherent and Lumentum, which rely heavily on Chinese and Southeast Asian manufacturing, face margin pressures due to reciprocal tariffs imposed by China. Market Dynamics and Competitive Pressures The global optical module market, projected to reach $12.1 billion in 2025, is grappling with paradoxical forces: surging demand for AI-driven 800G/1.6T modules and supply chain disruptions. Chinese suppliers dominate 70% of the 800G market, but tariffs have spurred U.S. efforts to reshore production. Companies like Intel are expanding silicon photonics initiatives, while Nvidia and Microsoft have lobbied for exemptions, citing the critical role of Chinese modules in AI server efficiency.   Price volatility is another consequence. Tariffs could inflate U.S. import costs by 10-15%, while Chinese firms may slash prices to retain market share. For example, Innolight’s 1.6T modules, set to launch in Q4 2025, are priced 20% below competitors due to Thailand-based production. This pricing war risks squeezing smaller players, with industry analysts predicting consolidation among second-tier manufacturers. Geopolitical and Technological Implications The tariffs have intensified competition for technical standards. The U.S. and EU are pushing for OIF-compliant silicon photonics, while China champions IEEE P802.3 protocols. Meanwhile, U.S. restrictions on advanced photonic chips (e.g., 100G EML lasers) have accelerated domestic innovation. Accelink now produces 60% of its own 25G/50G lasers, reducing reliance on Lumentum.   However, these advancements face hurdles. The U.S. Department of Energy’s Data Center Energy Efficiency Act mandates stricter power usage effectiveness (PUE) standards, favoring Chinese CPO modules that cut server energy consumption by 40%. This creates a Catch-22: U.S. tariffs aim to protect domestic industries but inadvertently disadvantage them in green technology adoption. Long-Term Outlook Industry experts predict a fragmented market by 2027, with 45% of global optical module production localized in North America and Europe. While tariffs may temporarily shield U.S. manufacturers, they risk delaying AI infrastructure deployment, as seen in Google and Nvidia’s postponed 1.6T module rollouts due to component shortages.   In the near term, the market will hinge on tariff enforcement and corporate agility. Companies with diversified supply chains (e.g., Eoptolink’s Mexico plant) and strong R&D pipelines (e.g., Innolight’s 3nm DSP integration) are best positioned to thrive. For the broader industry, the U.S. tariff saga underscores the delicate balance between geopolitical strategy and technological interdependence in a hyper-connected world.
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Latest company news about China's DeepSeek and Global AI Giants ChatGPT and Gemini: Technological Strengths and the Key Role of High-Speed Optical Modules
2025-02-11

China's DeepSeek and Global AI Giants ChatGPT and Gemini: Technological Strengths and the Key Role of High-Speed Optical Modules

  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. I. Comparison of the Three AI Models 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. II. High-Speed Optical Modules: The "Invisible Engine" of AI Evolution 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. III. Future Competition: Differentiation and Infrastructure Synergy 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. Conclusion 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.
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