South Korean AI Startup Motif Technologies Completes Bridge Funding Round with Planned NVIDIA Participation for Next-Gen LLM 'Motif 4'
South Korean AI foundation model company Motif Technologies announced the completion of a bridge funding round, with NVIDIA set to participate as an investor. The funds will support enhanced post-training for its next-generation open-weight large language model, 'Motif 4,' targeted for release in Q1 2027 with ambitions to rank among the world's top five.
South Korean artificial intelligence foundation model startup Motif Technologies announced on November 9 that it has completed a bridge funding round to finance the development of its next-generation large language model, "Motif 4." According to Platum, the round includes participation from existing investors and chip giant NVIDIA, though the specific amount remains undisclosed. A bridge round typically refers to interim financing secured before a formal subsequent major funding stage to sustain operations or advance critical projects.
"Motif 4" is the successor to "Motif 3," released by the company in August 2026, and is scheduled to be published as an open-weight model in the first quarter of 2027. Open weights mean the model parameters will be publicly available, allowing developers to download, use, and modify them. Motif Technologies has set an ambitious goal for "Motif 4" to enter the top five global models.
Previously, "Motif 3" was developed under the Ministry of Science and ICT (MSIT)'s "Independent AI Foundation Model" project in South Korea. According to Artificial Analysis data, "Motif 3" scored 47 points on the composite intelligence index, ranking 10th among all AI models globally and 4th among all open-weight models. Excluding US and Chinese enterprise models, it held the highest rank. The company stated that by adopting proprietary architectures such as Group Differential Latent Attention (GDLA), it completed the development of "Motif 3" using only 768 GPUs over five months.
However, due to time and resource constraints, "Motif 3" did not undergo sufficient post-training steps like reinforcement learning (RL). Post-training is a crucial phase where pre-trained models are refined for practical usability and human alignment. Motif Technologies plans to utilize the newly secured funds to significantly strengthen the post-training of "Motif 4," aiming to enhance its utility and commercial competitiveness.