Stop Flushing the KV Cache: How GitHub Trades VRAM for Compute to Cut Agentic Workflow Costs by 10x

1. 1Build a stateless agent architecture to reduce memory usage
2. 2Run simulations to determine optimal VRAM and compute tradeoffs
3. 3Configure workflow pipelines to prioritize compute over VRAM
4. 4Test and evaluate the performance of optimized workflows
5. 5Apply cost-benefit analysis to determine the effectiveness of optimizations

Data scientists and engineers working on agentic workflows and AI systems can benefit from this knowledge to optimize their own workflows and reduce costs

💡 Trading VRAM for compute can significantly reduce costs in agentic workflows

🚀 GitHub cuts agentic workflow costs by 10x by trading VRAM for compute! 💸