Home Insight FUNDING NEWS- Newly Funded Startups 2024 GigaML Secures $3.6 Mn Seed Funding Led by Nexus Venture Partners

[Funding alert] GigaML Secures $3.6 Mn Seed Funding Led by Nexus Venture Partners

[Funding alert] GigaML Secures $3.6 Mn Seed Funding Led by Nexus Venture Partners
[Funding alert] GigaML Secures $3.6 Mn Seed Funding Led by Nexus Venture Partners
[Funding alert] GigaML Secures $3.6 Mn Seed Funding Led by Nexus Venture Partners
Varun Vummadi and Esha Manideep, Co-Founders of Giga ML

GigaML, based in San Francisco and Bengaluru, secured $3.6 million in a seed funding round that was co-led by Nexus Venture Partners. In addition to Y Combinator, Liquid 2 Venture, 8vdx, and well-known angel investors like Garry Tan (Y Combinator president and CEO), this funding round was also supported by Y Combinator.

Businesses can use GigaML to deploy LLMs as powerful as GPT-4 right on their servers, doing away with the requirement to send private data to public servers like OpenAI.

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“At GigaML, our utmost priority is to ensure the privacy and security of enterprise data. Our goal is to position our company as the leading provider of On-Premise Secure LLMs, allowing enterprises to leverage the power of LLMs without compromising their data integrity,” said Varun Vummadi.

Varun Vummadi and Esha Manideep Dinne founded GigaML to assist businesses in securing their implementation of Large Language Models (LLMs) on-premise.

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Abhishek Sharma, Managing Director, Nexus Venture Partners, believes that there are significant challenges today around the need for more tooling and know-how of internal teams to put these models in production in a secure way. Enterprises can take Giga ML’s base model and further continue pre-training and fine-tune it to meet their specific needs.

About GigaML

Custom LLMs can perform as well as ChatGPT-4 with the help of the GigaML platform. Enterprises use their open-source comparisons, which exceed all other simulations, including Claude 2, for initial training, modification, and on-premises model deployment.

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