Video: the LLM 2.0 Revolution
What if you could build a secure, scalable RAG+LLM system – no GPU, no latency, no hallucinations?
In this session, Vincent Granville shares how to engineer high-performance, agentic multi-LLMs from scratch using Python. Learn how to rethink everything from token chunking to sub-LLM selection to create AI systems that are explainable, efficient, and designed for enterprise-scale applications.
What you’ll learn:
How to build LLM systems without deep neural nets or GPUs
Real-time fine-tuning, self-tuning, and context-aware retrieval
Best practices in chunking, crawling, and UI design
A case study using financial reports from Nvidia
With Vincent Granville, Co-founder & AI Lead at BondingAI.io.
A serial founder, author, and former post-doc at Cambridge, Vincent’s work spans open-source tools, Fortune 100 deployments, and millions of downloads.
Watch the video, here. See also Vincent’s YouTube video on “Scaling, Optimization & Cost Reduction for LLM/RAG & Enterprise AI”, here. For other podcasts and webinars by Vincent Granville, visit our podcasts section, here. Books on the topic are available here.
Related articles:
Main differentiators between LLM 2.0 and LLM 1.0 – link.
How to Get AI to Deliver Superior ROI, Faster – link.
Benchmarking xLLM and Specialized Language Models – link.
Doing Better with Less: LLM 2.0 for Enterprise – link.
How to Design LLMs that Don’t Need Prompt Engineering – link.
From 10 Terabytes to Zero Parameter: The LLM 2.0 Revolution – link.
10 Must-Read Articles and Books About Next-Gen AI in 2025 – link.
