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Spotlight
As AI Reaches Locals, It’s Time to Build Local Large Language Models
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Have you ever noticed that, no matter how advanced leading generative AI platforms such as ChatGPT, Gemini, Claude, and Meta AI become, their responses are still sometimes inaccurate? This problem is found more often in countries where English is not the primary language. Behind large language models (LLMs) are “brains” trained based on English-language data. They therefore tend to interpret the world through their developers’ Western lens. Given that their responses may lack cultural nuance, contextual understanding, and sensitivity to diverse identities, many countries have now developed their own language models.
Why are Local LLMs Better?
Beyond offering more natural language capabilities and a deeper understanding of diverse social and cultural contexts, another key reason for so many countries to urgently develop their local large language models (Local LLMs) is to lay the foundation for a stable and sustainable AI ecosystem — one that reduces dependence on external providers and allows nations to steer technological development in line with their own ethics and values. Local LLMs also help preserve local knowledge, cultural heritage, and wisdom while lowering operational costs, improving stability through domestic server infrastructure, and safeguarding sensitive data from external exposure. Their flexibility and adaptability make them particularly valuable for long-term applications across both enterprise and government sectors.
“LOCAL LLMs” Around The World
Today, local LLMs are being developed in various forms to serve different objectives — from enhancing AI capabilities within specific countries to expanding accessibility across entire regions.
India – BharatGen and Sarvam AI, LLMs currently under development, are designed to support more than 20 local languages for Indian users specifically.
Korea – HyperCLOVA X is an LLM built especially for the Korean language and cultural context.
Africa – N-ATLAS S is trained to support several major Nigerian languages, namely Yoruba, Hausa, Igbo, and Pidgin.
China – Deepseek LLM is an LLM developed for Chinese-language context, capable of handling both Mandarin syntax and classical Chinese texts. It has now gained recognition as one of the world’s leading AI models.
Southeast Asia – SeaLLMs (Southeast Asian Large Language Models) supports nine Southeast Asian languages, helping reduce language barriers and broaden Southeast Asian users’ access to advanced technology.
How Far Has Thai AI Come Today?
Several LLMs focused on Thai language and context have now been developed by both public and private sectors. Each offers distinct strengths tailored to different applications, including:
- PathummaLLM by the National Electronics and Computer Technology Center (NECTEC) and Ministry of Higher Education, Science, Research and Innovation stands out for its solid understanding of Thai context, official language, academic research, and document processing.
- Typhoon by SCBX is an open-source LLM designed for business sector. Several versions of Typhoon have already been introduced, including Typhoon 2.5, the latest model capable of seamless integration with agentic workflows while delivering high performance at relatively low cost. Another version, Typhoon Isan, focuses on developing an AI model for the Isan dialect, aiming to preserve cultural heritage alongside future digital and economic development.
- THaLLE by KBTG specializes in Thai language and finance. It became the first Thai LLM to pass the CFA Level II examination, an internationally recognized assessment in finance and investment knowledge.
When it comes to AI adoption, the focus is no longer about technological competition but practical application. The best model is not always the smartest, but the one that offers the best understanding, cost-effectiveness, and efficiency in solving real-world problems and is ready to deliver tangible, sustainable results.