The Fifth Elephant 2024 Annual Conference (12th &13th July)

Abstract:

This session delves into the complexities involved in building a scalable, decentralized GPU cloud tailored for the efficient training, fine-tuning and deployment of AI models, more specifically large language models (LLMs). We will explore the significant technical hurdles our team overcame, including ensuring cost-effectiveness, optionality and accessibility of GPU resources. This infrastructure is crucial for unlocking the full potential of building state of the art AI applications and it has helped businesses using our platform save more than $500K in their AI computing workload costs.

We will explore how this computing engine serves as the base layer for MonsterAPI’s vertically stacked no-code LoRA/QLoRA powered LLM finetuning and deployment solutions designed to simplify the process and optimize the GPU processing cost.

To further democratize the access of LLM finetuning and deployment capabilities, we also introduce MonsterGPT - A chat powered AI engineering assistant for LLM finetuning and deployments.

Intended Audience:

This session is ideal for AI/ML developers, data scientists, DevOps and product managers interested in enhancing model performance without the complexities of traditional infrastructural setups. It will also benefit business leaders looking to implement AI solutions swiftly and cost-effectively.

Problems Addressed:

We addressed the challenges of high computing costs, low accessibility of GPUs and complex MLOps skills needed for AI model development and deployment. Our decentralized GPU cloud and no-code AI engineering platform remove major obstacles, making advanced computing affordable and simple for developers of all levels and businesses of any size. This approach reduces the need for specialized fine-tuning knowledge, cuts infrastructure costs, and speeds up deployment, making powerful AI tools more accessible to everyone.

Scope of the Talk:

1. Identifying the need for accessible, cost-effective GPU computing in AI.
2. Technical deep dive into the development of our decentralized GPU cloud infrastructure.
3. How LoRA/QLoRA technology integrates within this infrastructure to facilitate no-code LLM fine-tuning.
4. Demonstrating ‘MonsterGPT’ in action: A user-friendly AI Agent that performs LLM operations using natural language instructions.
5. Real-world applications and case studies that showcase the impact of our cost-efficient AI solutions.

Attendees will gain:

• Understanding of the technical challenges and solutions involved in creating a decentralized computing environment.
• Learn how to leverage no-code platforms for efficient LLM fine-tuning and deployment.
• Strategies for implementing scalable AI solutions without in-depth technical expertise in infrastructure management.
• Gain insights into managing infrastructure that supports advanced AI technologies without extensive expertise.

Format of the Session:

Technical Presentation followed by a Live Demonstration and Q&A