H2O.ai Launches Enterprise LLM Studio

MOUNTAIN VIEW, Calif.--(BUSINESS WIRE)--H2O.ai, a leader in open-source Generative AI and Predictive AI platforms, today announced H2O Enterprise LLM Studio, running on Dell infrastructure. This new offering provides Fine-Tuning-as-a-Service for businesses to securely train, test, evaluate, and deploy domain-specific AI models at scale using their own data.

Built by the world’s top Kaggle Grandmasters, Enterprise LLM Studio automates the LLM lifecycle — from data generation and curation to fine-tuning, evaluation, and deployment. It supports open-source, reasoning, and multimodal LLMs such as DeepSeek, Llama, Qwen, H2O Danube, and H2OVL Mississippi. By distilling and fine-tuning these models, H2O.ai customers obtain reduced costs and improved inference speeds.

“Distilling and fine-tuning AI models are transforming enterprise workflows, making operations smarter and more efficient. H2O Enterprise LLM Studio makes it simple for businesses to build domain-specific models without the complexity.”

Sri Ambati

Founder and CEO of H2O.ai

Key Features

• Model Distillation: Compress larger LLMs into smaller, efficient models while retaining crucial domain-specific capabilities
• No-Code Fine-Tuning: Adapt pre-trained models through an intuitive interface, no AI expertise required
• Advanced Optimization: Distributed training, FSDP, LoRA, 4-bit QLoRA
• Scalable AI Training & Deployment: High-performance infrastructure for enterprise workloads
• Seamless Integration: Fast APIs for production AI workflows

Demonstrated Benefits

• Cost: Fine-tuned open-source LLMs have reduced expenses by up to 70%
• Latency: Optimized processing cut inference time by 75%
• Self-Hosted Solution: Preserves data privacy, ensures flexibility, and avoids vendor lock-in
• Reproducibility: Other teams can re-use refined open-source models to iterate on new problems
• Scalability: Handles 500% more requests than the previous solution

As organizations scale AI while preserving security, control, and performance, the need for fine-tuned, domain-specific models grows. H2O.ai customers address these needs by distilling large language models into smaller open-source versions, reducing costs and boosting scalability without compromising accuracy.

Model distillation shrinks complex models into efficient ones while retaining key functionality, and fine-tuning further specializes them for targeted tasks. These techniques produce high-performing, cost-effective AI solutions built for specific business requirements.

Learn more about Fine-Tuning-as-a-Service with H2O.ai at NVIDIA GTC

Stop by H2O.ai at NVIDIA GTC (booth 3246) to learn about model distillation and fine-tuning SLMs and to see H2O.ai customer sessions:

• Monday, Mar 17, 2025 (4:00 PM - 4:40 PM PDT)

Driving Telco Innovation: Distilling Large Models Into Economical Small Language Models for Agentic Workflow Automation Presented by Hien Lam (AT&T) and Ryan Chesler (H2O.ai Kaggle Grandmaster)

REGISTER HERE

• Wednesday, Mar 19, 2025 (2:00 PM - 2:40 PM PDT)

Delivering Real Business Outcomes With AI in Telecom Panel Featuring Andy Markus, SVP & Chief Data and AI Officer, AT&T

REGISTER HERE

• Wednesday, Mar 19, 2025 (3:00 PM - 3:40 PM PDT)

AI Agents and Digital Humans Shaping the Future of Interaction in Telecoms Panel Featuring Mark Austin, VP Data Science, AI & Automation, AT&T

REGISTER HERE