In the fast-evolving realm of AI, the spotlight shines brightly on large language models (LLMs) like ChatGPT, revered for their text-generation prowess. A recent survey of approximately 1,000 enterprise organizations revealed that a staggering 67.2% consider embracing LLMs a top priority by early 2024.
However, hurdles loom large. The survey highlights a critical impasse: a lack of adaptability and the inability to safeguard proprietary knowledge and intellectual property hinder the seamless integration of LLMs into operational workflows for many businesses.
Enter Varun Vummadi and Esha Manideep Dinne with a resolve to address this enterprise LLM adoption challenge. Their brainchild, Giga ML, stands as a beacon, offering a platform empowering companies to deploy LLMs on-premise. The vision? To cut costs and fortify privacy barriers.
“Data privacy and customization are formidable challenges enterprises face with LLM adoption,” shared Vummadi in an exclusive interview. “Giga ML confronts these head-on.”
Giga ML boasts its suite of LLMs, the “X1 series,” tailored for tasks such as code generation and addressing customer queries. Leveraging Meta’s Llama 2 as the foundation, these models purportedly outshine popular LLMs in specific benchmarks, notably excelling in the MT-Bench test set for dialogues. Yet, evaluating X1’s qualitative edge remains elusive; a trial of Giga ML’s online demo encountered technical glitches, timing out despite various prompts.
Beyond the quest for superiority, does Giga ML aim to disrupt the universe of open-source offline LLMs?
Conversations with Vummadi suggest otherwise. Giga ML isn’t fixated on crafting the ultimate LLMs; instead, it seeks to furnish tools empowering businesses to fine-tune LLMs locally, bypassing reliance on external resources and platforms.
“Our mission is to facilitate enterprises in securely and effectively deploying LLMs on their own on-premises infrastructure or virtual private clouds,” clarified Vummadi. “Giga ML streamlines training, fine-tuning, and execution through an intuitive API, alleviating complexities.”
Highlighting the privacy perks of offline model runs, Vummadi targets businesses wary of data sharing concerns.
Predibase, a low-code AI development platform, revealed that less than a quarter of enterprises feel comfortable utilizing commercial LLMs due to apprehensions about sharing sensitive data. A whopping 77% either abstain from or have no intention of embracing commercial LLMs in production, citing privacy, cost, and adaptability qualms.
“IT managers at the C-suite level value Giga ML for secure on-premise LLM deployment, customizable models tailored to their needs, and swift inference ensuring data compliance and peak efficiency,” Vummadi emphasized.
Having secured approximately $3.74 million in VC funding from Nexus Venture Partners, Y Combinator, Liquid 2 Ventures, 8vdx, and other backers, Giga ML gears up for expansion. Their immediate plans involve team scaling and accelerated product R&D efforts. Vummadi disclosed that a portion of the funds will bolster their client support, serving current clientele in finance and healthcare, albeit undisclosed.
