대규모 언어 모델(LLM) 시장 규모, 점유율 및 동향 분석 보고서 : 배포, 업종, 용도, 지역별, 전망 및 예측(2023-2030년)
Global Large Language Model Market Size, Share & Trends Analysis Report By Deployment, By Vertical (Retail & E-commerce, Media & Entertainment, Finance, Healthcare, and Others), By Application, By Regional Outlook and Forecast, 2023 - 2030
상품코드:1431383
리서치사:KBV Research
발행일:2024년 02월
페이지 정보:영문 252 Pages
라이선스 & 가격 (부가세 별도)
한글목차
대규모 언어 모델(LLM) 시장 규모는 2030년까지 331억 달러에 달할 것으로 예상되며, 예측 기간 동안 연평균 34.3%의 시장 성장률을 나타낼 전망입니다.
그러나 대규모 언어 모델(LLM) 학습에는 의도하지 않은 정보 유출의 위험이 있습니다. 모델은 학습 데이터에 존재하는 기밀 정보를 부주의하게 기억하거나 과도하게 적합화할 수 있으며, 이는 잠재적인 프라이버시 침해로 이어질 수 있습니다. 훈련된 모델은 가중치에 입력 데이터의 흔적을 남길 수 있고, 이러한 가중치에서 기밀 정보를 추출할 수 있기 때문에 프라이버시 문제가 발생할 수 있습니다. 추론 단계에서 모델이 사용자의 쿼리나 입력을 처리할 때, 모델이 그러한 데이터를 안전하게 처리하도록 설계되지 않은 경우, 의도치 않게 기밀 정보를 공개할 위험이 있습니다. 차등 프라이버시와 같은 기술은 학습 프로세스에 제어된 노이즈를 주입하여 개별 데이터 포인트를 보호하고 특정 사례를 기억하지 못하도록 하는 것을 목표로 합니다. 따라서 이러한 요인들은 시장 성장을 저해하는 요인으로 작용할 수 있습니다.
The Global Large Language Model Market size is expected to reach $33.1 billion by 2030, rising at a market growth of 34.3% CAGR during the forecast period.
Businesses across various sectors are increasingly deploying chatbots and virtual assistants to automate customer service interactions. Thus, the chatbots and virtual assistant segment acquired $1,045.4 million in 2022. These AI-driven systems can handle routine queries, provide information, and guide users through processes, freeing up human agents for more complex tasks. Thus, these aspects will boost the demand in the segment.
Prominent language models, boosted by deep learning methodologies, have exhibited an exceptional capacity to comprehend and produce text that closely resembles that of humans. Their extensive training on diverse datasets enables them to grasp nuances, context, and subtleties in language, allowing for more sophisticated language understanding. In addition, large language models are a fundamental component of chatbots and virtual assistants. These applications benefit from the models' capacity to comprehend user input, generate contextually relevant responses, and provide more interactive and human-like conversations. As businesses increasingly adopt chatbots for customer support, information retrieval, and engagement, the demand for advanced language models grows. Additionally, it can generate content quickly and efficiently. This is particularly beneficial for businesses looking to produce a large volume of content within a short time frame, such as articles, blog posts, or marketing materials. Businesses can leverage large language models to scale up their content creation efforts. These models can consistently produce content with a similar tone, style, and quality, maintaining a cohesive brand identity across various pieces of content. This adaptability ensures the generated content aligns with different industries' specific requirements and conventions. Hence, these aspects will lead to enhanced growth in the market.
However, there is a risk of unintended information leakage during the training of large language models. The models may inadvertently memorize or overfit sensitive information present in the training data, leading to potential privacy breaches. Trained models might retain traces of the input data in their weights, and there's a possibility of extracting sensitive details from these weights, raising privacy concerns. During the inference phase, when models process user queries or inputs, there's a risk of unintentionally disclosing sensitive information if the models are not designed to handle such data securely. Techniques like differential privacy aim to protect individual data points by injecting controlled noise into the training process, helping to prevent the memorization of specific examples. Thus, these factors will reduce growth in the market.
By Deployment Analysis
Based on deployment, the market is divided into cloud and on-premise. The on-premise segment recorded the maximum revenue share in the market in 2022. Sectors that handle confidential data, including healthcare, finance, and legal, frequently give precedence to on-premises solutions to maintain direct oversight of their information. On-premises deployment ensures that sensitive language data remains within the organization's infrastructure, addressing data security and privacy compliance concerns. Therefore, these factors will assist in the growth of the segment.
By Vertical Analysis
On the basis of vertical, the market is divided into healthcare, finance, retail & e-commerce, media & entertainment, and others. In 2022, the media and entertainment segment witnessed a substantial revenue share in the market. It facilitate translation on a broader scale, enabling media and entertainment content to reach global audiences. This is especially valuable for streaming platforms, news outlets, and content providers seeking to expand their reach and cater to diverse linguistic demographics. Thus, these factors will help expand the segment.
By Application Analysis
Based on application, the market is segmented into customer service, content generation, sentiment analysis, code generation, chatbots & virtual assistant, and language translation. The content generation segment procured a promising growth rate in the market in 2022. The demand for content spans multiple industries, including marketing, advertising, e-commerce, journalism, and more. Large language models offer versatility in generating content for different purposes, making them valuable across various applications. Owing to these aspects, the segment will witness enhanced growth in the coming years.
By Regional Analysis
By region, the market is segmented into North America, Europe, Asia Pacific, and LAMEA. The North America segment procured the highest revenue share in the market in 2022. The region is a hub for cutting-edge research and development in AI and natural language processing. Advances in large language models, including innovations in model architectures, training techniques, and applications, often originate from research institutions and companies in North America. Hence, these factors will lead to enhanced growth in the segment.
Recent Strategies Deployed in the Market
Dec-2023: Google LLC signed a partnership with Mistral AI, an AI startup specializing in developing innovative solutions for various industries through advanced machine learning algorithms and data analytics. Through this partnership, Google would leverage its Cloud AI-optimized infrastructure, including TPU Accelerators, to advance the testing, development, and scalability of its LLMs (large language models).
Nov-2023: Amazon Web Services, Inc. extended the partnership with Salesforce, Inc., a cloud-based software company renowned for its customer relationship management (CRM) solutions. Through this partnership, AWS would be able to provide their customers, access to robust avenues for innovation, teamwork, and the development of customer-centric applications, leveraging an extensive array of cloud services.
Aug-2023: Google LLC came into partnership with Cognizant, a global services company specializing in digital transformation, consulting, and technology solutions for businesses across various industries. Through this partnership, both companies would be able to provide their healthcare customers with a spectrum of technologies, enabling transformative changes previously unattainable.
Aug-2023: IBM Corporation signed a collaboration with Microsoft, a multinational technology company known for its software products, including the Windows operating system and Office productivity suite, as well as its cloud computing services and hardware devices. Under this collaboration, Microsoft would be able to expedite the implementation of generative AI for mutual clients, offering a novel solution that equips them with the necessary technology and expertise to innovate their business processes and scale generative AI seamlessly.
Aug-2023: NVIDIA Corporation came into partnership with VMware, Inc., a software company specializing in virtualization, cloud computing, and software-defined networking solutions. Through this partnership, VMware would provide a wide array of customers, spanning financial services, healthcare, manufacturing, and other sectors, with comprehensive software and computing solutions essential for harnessing the power of generative AI through tailored applications developed with the proprietary data.
Jun-2023: Amazon Web Services, Inc. signed a collaboration with Accenture, a global services company offering consulting, technology, and outsourcing solutions across various industries. Through this collaboration, both companies would equip their clients with cutting-edge AI technologies to tackle paramount organizational obstacles, catalyze business metamorphosis, and expedite innovative endeavors.
