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

1. 1Build a stateless agent architecture using compute resources
2. 2Configure the agent to use a small amount of VRAM
3. 3Test the agent's performance with varying compute and VRAM allocations
4. 4Apply cost optimization techniques to agentic workflows
5. 5Compare the costs of different compute and VRAM configurations

Data scientists and machine learning engineers on a team can benefit from this knowledge to optimize their own agentic workflows and reduce costs

💡 Trading VRAM for compute can significantly reduce agentic workflow costs

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