From ChatGPT to Private Deployment of Large AI Models: Who Is the True Technological Savior for Enterprises?
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From ChatGPT to Private Deployment of Large AI Models: Who Is the True Technological Savior for Enterprises?
Current Status and Development Potential of Private Deployment of Large AI Models
Navigating Web3 tides with focused insightsMeta recently launched LLaMa2, a free, open-source, commercially usable large language model comparable to GPT-3.5. In addition to announcing that LLaMa2 would be freely available for open commercial use, Meta also released some related data about the model. In terms of parameter versions, LLaMa2 offers three distinct variants: 7 billion, 13 billion, and 70 billion parameters.
As large language models like LLaMa2 continue to disrupt an increasing number of production domains, how enterprises can adopt large models and implement private AI deployments has become an increasingly hot topic. Recently, well-funded central state-owned enterprises and industry clients are actively seeking private large model solutions to build specialized models based on proprietary industry data—an alternative path for commercializing large models and one with substantial market potential.
Currently, many companies are positioning themselves in the field of private AI large model deployment. For example, Contextual AI is conducting research on Retrieval-Augmented Generation (RAG) for enterprise private deployment; Cohere is training models according to specific customer requirements; Reka’s model distillation technology provides customers with a leading-edge platform for private code generation, significantly enhancing enterprise R&D efficiency. This article by R3PO dissects this emerging sector and shares insights into the current state and development potential of private AI large model deployment.
1. The Digital Future of Enterprises Cannot Do Without Private Deployment of AI Large Models
Many large traditional enterprises, due to concerns over data security, cannot utilize public cloud-based AI services. These organizations often have weak foundational AI capabilities and lack accumulated technical expertise and talent, yet intelligent transformation remains a critical—sometimes urgent—need. In such cases, leveraging AI technology firms to deploy AI middleware platforms privately within the enterprise represents a cost-effective and efficient strategy.
Tencent's Tang Daosheng recently pointed out in a speech: "General-purpose large models may solve 70%-80% of problems across 100 scenarios, but they might not fully meet the demands of a specific enterprise scenario." General-purpose models are typically trained on broad public literature and web data, lacking depth in professional knowledge and industry-specific datasets, thus falling short in domain specificity and precision. However, users expect high accuracy from enterprise-provided services and offer little tolerance for errors. Delivering incorrect information publicly could lead to serious consequences. By fine-tuning industry-specific large models using internal data, enterprises can develop highly reliable intelligent services. Moreover, compared to general-purpose models, dedicated models usually involve fewer parameters, resulting in lower training and inference costs and easier optimization.
Additionally, industry-specific large models and model development tools can prevent leaks of sensitive corporate data through private deployment, access control, and data encryption. Furthermore, applying large models in real-world scenarios requires a series of steps including algorithm design and model deployment, each of which must be executed flawlessly. Continuous model iteration and tuning demand systematic, engineering-grade tooling.
2. What Is the Significance of Private Deployment of AI Large Models?
Recently, Reka, a company offering customized large model services for enterprises, secured $58 million in funding—reflecting the growing scale of the enterprise private AI model market.
Although large language models like GPT-4 demonstrate strong capabilities in text analysis and generation, their high training costs and difficulty in vertical-domain specialization make them poorly suited for tasks such as writing advertising copy aligned with a brand's unique tone. In these cases, their very "generality" becomes a drawback.
To address the challenges of integrating AI into enterprise-specific vertical applications, private deployment has emerged as the preferred solution. Enterprise AI private deployment refers to the process of migrating AI technologies from public cloud platforms to the enterprise’s own private infrastructure. This approach enables greater data security and privacy protection while allowing better control and customization of AI applications. It typically involves building internal AI infrastructure, data storage and processing systems, and employing AI specialists to manage and maintain the entire system.
Reka outlines the significance of enterprise AI private deployment through five key aspects:
1. Enhanced Data Privacy and Security
By deploying AI systems internally, sensitive data remains within the enterprise’s secure boundaries, reducing risks of data breaches and vulnerabilities. This increases trust and protection when handling tasks involving confidential information.
2. Increased Customization and Flexibility
Private AI deployment allows organizations to tailor AI applications to their specific needs. This level of customization enables enterprises to better adapt to unique business contexts and make flexible adjustments or expansions as required.
3. High Performance and Low Latency
Deploying AI systems on internal infrastructure enables faster data transmission and processing speeds. This is crucial for applications requiring real-time decision-making and rapid response, thereby improving overall efficiency and competitiveness.
4. Improved Cost Efficiency
While private AI deployment requires upfront investment, it can yield positive long-term cost impacts. Compared to ongoing reliance on public cloud platforms, private deployment reduces operational expenses and allows better budget control and planning.
5. Data Governance and Compliance
Private AI deployment empowers enterprises to better manage and control data governance to meet regulatory and compliance requirements. This is especially important for industries dealing with personal privacy protection and strict data usage regulations.
3. Personalized Customization and Optimization: Reka’s Model Distillation Technology Brings Significant Potential to Enterprise Recommendation Models
Founded by researchers from DeepMind, Google, Baidu, and Meta, Reka is currently co-led by DST Global Partners and Radical Ventures, with strategic partner Snowflake Ventures and investors including former GitHub CEO Nat Friedman participating in the round.
Reka has already developed its first commercial product, Yasa. While not yet achieving all initial goals, Yasa has made notable progress in customized modeling. Yogatama stated that Yasa is a multimodal AI assistant capable of understanding images, videos, and tabular data—not just words and phrases—after training. Additionally, it can generate ideas, answer basic questions, and provide insights based on a company’s internal data.
Unlike models such as GPT-4, Yasa can be easily personalized for proprietary data and applications. Beyond text, Yasa functions as a multimodal AI “assistant” trained to understand images, videos, and tables. According to Yogatama, it can be used to generate ideas, answer fundamental queries, and extract insights from internal corporate data.
Reka’s next step is to focus on AI systems that can accept and generate more data types and continuously self-improve, staying up-to-date without requiring retraining. To support this, Reka offers a service enabling its models to adapt to custom or proprietary datasets. Customers can run these customized models either on their own infrastructure or via Reka’s API, depending on application and project constraints.
4. The Market for Private Deployment of AI Large Models Is Gaining Strong Momentum
Enterprise-customized AI deployment technology brings higher efficiency and flexibility to large-scale recommendation models through advantages in resource efficiency, real-time performance, personalization, and interpretability, thereby enhancing both recommendation system performance and user experience.
In summary, numerous companies are advancing along the path of customized AI models, enabling every enterprise to become an AI-powered organization without having to build models from scratch. Clearly, as this trend continues, the market size for enterprise-private AI models will only keep expanding.
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