The headline needs to capture: Waste project, massive AI models, low-RAM, NVMe streaming, key facts.

Short summary

A new open-source project called Waste enables running massive AI models like the 2.78-trillion-parameter Kimi K3 in low-RAM environments by streaming activated weights directly from NVMe storage. Written in C with no dependencies, it has gained over 1,600 GitHub stars. Performance trade-offs around NVMe latency mean teams must benchmark before adopting.

• •Waste streams model weights from NVMe storage, bypassing full RAM loading
• •Enables running Kimi K3 (2.78T params) on constrained hardware; written in C, dependency-free
• •NVMe streaming introduces latency trade-offs; teams should benchmark before production use

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