The excitement surrounding DeepSeek persists long after the Spring Festival, signaling a transformative shift in the AI landscapeThe deployment of DeepSeek models across various mainstream cloud platforms both domestically and internationally marks a significant milestoneObservers note that this development represents a broader AI democratization initiative centered on the principle of "low cost, high efficiency."

One striking feature of DeepSeek is its ability to substantially reduce computational requirements for tasks, which translates to lower hardware investment costs when compared to traditional AI training methodsThis allows DeepSeek to offer services comparable to those of ChatGPT-o1 without imposing fees on users, thereby making advanced AI accessible to a wider audience.

2025 is projected to be a pivotal year for AI models, driven by the cost-reduction and efficiency gains championed by DeepSeekIn response to these market dynamics, numerous vendors—both domestic and international—have begun to leverage the DeepSeek framework, investing real capital to attract consumers with high-value offerings.

Among the domestic technology frontrunners, Baidu stands out distinctlyIts commitment to the commercialization of large AI models is evident, as illustrated by its announcement on February 3 that the Baidu Intelligent Cloud's Qianfan platform has officially launched DeepSeek-R1 and DeepSeek-V3 modelsMore notably, they introduced a pricing strategy that slashes costs down to 30% of the official price for DeepSeek-V3 and 50% for DeepSeek-R1, alongside a limited-time free service offering for users.

The DeepSeek models integrated into Baidu’s framework are designed with enterprise-level security enhancements and high availability, coupled with comprehensive log analysis and alarm mechanisms—ensuring that users can develop intelligent applications in a secure and stable environment.

According to public data, the overall cost of DeepSeek-R1 is approximately just 1/30th that of OpenAI's o1 model, a figure that has sent shockwaves through industries globally

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Renowned for his contributions to AI research in Japan, Professor Yoshua Matsuo from the University of Tokyo recently commented on the impressive performance of DeepSeek technology, noting its operational capabilities are on par with OpenAI's offeringsEach release of a new AI model comes accompanied by detailed academic papers outlining the applied technologies and innovations incorporated within.

Matsuo also praised DeepSeek for its commitment to open-source developmentThe latest model, “R1”, has undergone reinforcement learning to enhance its inference capabilities, resulting in inference performance equivalent to OpenAI's o1. This underscores a critical competitive edge—providing a more convenient, efficient, and affordable product experience has become a key differentiator in a crowded AI marketplace.

Baido's approach is aimed at minimizing AI model testing costs for users, intersecting seamlessly with the current trend of democratizing AI technologyMarket research from Gartner suggests that by 2027, over half of the AI models used by enterprises will be geared toward specific industry applications, a significant increase from less than 1% in 2023.

Despite these promising advancements, companies face considerable obstacles in translating large models into effective business applications—specifically the challenges of high capital investment and unclear short-term returnsLowering the technical costs associated with AI remains the fundamental driver of innovationAt the World Government Summit held in Dubai on February 11, Baidu's founder, Robin Li, highlighted that most innovations in IT and AI are closely tied to cost reductions, asserting that improved productivity directly correlates with decreased costs.

Li stated, “Today, the pace of innovation is faster than ever beforeAccording to Moore's Law, performance doubles every 18 months while prices halveCurrently, the cost of model inference is dropping by more than 90% annually.”

A price war may be emerging as cloud vendors announce their adoption of DeepSeek

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When comparing the service pricing of Baidu Intelligent Cloud to other providers, Baidu’s advantages become evidentThe company's engagement with the market reflects a proactive strategy to enrich its AI model ecosystem and broaden user choices.

Overall, Baidu’s proposals reflect an acute awareness of the market's demands, providing high efficiency and effective cost solutions that help users strike a balance between performance and expenditure.

Baidu’s Qianfan ModelBuilder, part of their comprehensive suite for large model solutions, offers users services including model invocation and performance optimizationThis platform facilitates high-efficiency offerings in AI models and a toolchain for fine-tuning, encompassing data processing, model refinement, evaluation, and quantization.

As DeepSeek employs efficiency characteristic of China to dismantle the costly barriers shaped by capital and computational power within the global AI model sphere, the overall sector has witnessed marked changesThe access and usage costs for Baidu to call R1 and V3 models have plummeted to half and a third, respectively, of the official pricing—setting a new standard in the market.

It becomes clear that in the wake of DeepSeek, a reliable product experience gained at a logical cost supersedes the previous focus on excessive spending and sheer computational accumulationUnder its current trajectory, Baidu’s product and service offerings have achieved noteworthy market recognition.

As per official data, the Qianfan large model platform has assisted clients in fine-tuning over 33,000 models and launching 770,000 enterprise applications across various sectors including finance, government, automotive, and technology, providing a seamless, efficient, and economical experience for large model development.

From an industry standpoint, the attractive efficiency and cost proposals from Baidu not only optimize its own investment but also pave the way for new strategies and directions for the broader field.

When examining the global AI competition landscape, it's evident that maintaining lower computational costs while achieving higher performance remains pivotal for enterprises wishing to secure a competitive edge

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Effectively lowering model invocation costs and delivering superior efficiency, as emphasized by Baidu, relies heavily on their robust computational resources and innovations in inference engine optimization, services architecture, and comprehensive safety assurances.

Central to Baidu's strategy is its self-developed Wanka cluster, which has been instrumental in achieving reduced computational expensesA glance at the global competitive landscape for large models reveals that a robust cluster has become a critical resource for companiesThe ability of the Wanka cluster to continuously decrease training cycles for models with billions of parameters also facilitates rapid iteration of AI-native applications.

In early February, Baidu announced the activation of its third-generation Kunlun Wanka cluster, marking a milestone as the first and only self-developed cluster in the nationThis advancement not only bolsters Baidu's computational capabilities but also promises to further reduce model costs.

In terms of inference engine performance, drawing from its accumulated expertise, Baidu has optimized DeepSeek model calculations for maximum efficiencyBy overlapping different resource types, such as computation, communication, and memory, alongside a split architecture for pre-fill and decode tasks, Baidu has significantly enhanced model throughput while minimizing inference costs.

The inference services themselves have undergone comprehensive refinements, contrasting push/pull performance within the architectureEvidence suggests that the pull method surpasses the push approach on key metrics, such as request success rates, response latency, and throughput.

To further boost reliability and user experience, Baidu has incorporated a thoughtful mechanism for retrying requests that fail, thereby enhancing system resilience and service-level agreement complianceAdditionally, for scenarios involving repeated prompt prefixes, they implemented KV-cache reuse technology, streamlining flow scheduling to prevent redundant calculations.

On the critical issue of data security, the platform benefits from Baidu's extensive background in large model security, integrating proprietary content safety algorithms to achieve enhanced security and enterprise-level reliability

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