Compute Optimization Podcast Summaries
Compute Optimization on Yedapo: 2 summarized podcast and YouTube episodes. Each includes key takeaways, core concepts and notable quotes with timestamps.
Fine-Tune the biggest open-source models (even with a bad PC)
David Ondrej
Jul 7, 2026
David Andre demonstrates how to perform supervised fine-tuning on large-scale open-source models like Kim K2.7 using LoRA (Low-Rank Adaptation). By leveraging cloud-based GPU platforms and high-quality datasets, developers can build custom, specialized AI models without the prohibitive $100,000 cost of local hardware, significantly outperforming generic models in specific domains.
Key insight: Fine-tuning a trillion-parameter model using LoRA requires training only a small adapter layer rather than the entire base model, reducing the cost of high-compute GPU usage to less than $40 for a full training cycle.
DeepSeek Just Solved AI's Billion Dollar Problem
Two Minute Papers
Jun 22, 2026
Dr. Károly Zsolnai-Féhér reveals that current AI systems suffer from massive underutilization because memory access bottlenecks—not processing power—cripple performance. By implementing a smart traffic-control system that redirects data streams to idle decoding hardware, researchers at DeepSeek have successfully boosted GPU utilization from 40% to 80% for complex agentic workloads.
Key insight: Modern AI systems often waste billions in hardware costs because their 'straw'—the memory bandwidth—is too narrow, leaving GPUs idling while they wait for data to read.