New offering enables enterprises to fine-tune and deploy LLMs on Dell infrastructure - bringing secure and tailored AI models to business-critical workflows.
By distilling and fine-tuning open source, reasoning, and multimodal LLMs, H2O.ai customers have achieved measurable ROI through reduced costs and faster inference time
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," said Sri Ambati, CEO and Founder of H2O.ai. "H2O Enterprise LLM Studio makes it simple for businesses to build domain-specific models without the complexity."
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.
