Smarter AI doesn't always need bigger hardware. Sometimes, it needs better optimization. PrismaX just made a significant improvement to its π0.5 inference stack. By fixing two memory inefficiencies in its code, the team reduced peak VRAM usage from approximately 22 GB to 11.2 GB. That's almost a 50% reduction! Why does this matter? • π0.5 can now fit on 16 GB GPUs, including the RTX 5080 and 5070 Ti. • Desktop hardware becomes a more practical option for running the model. • Lower hardware requirements could make AI-powered robotics more accessible. • The team estimates around 100 ms per inference step on the new configuration. What stands out to me is the approach: instead of immediately demanding more expensive hardware, PrismaX focused on making its existing system more efficient. That's the kind of engineering progress worth paying attention to. Better optimization. Lower hardware barriers. More room for AI innovation. Building smarter with @PrismaXai .❤️