Emerging frontier open-source models like GLM-6 may exceed 2 trillion parameters, potentially outstripping consumer-grade server capacities like 768GB VRAM. Maintaining 4-bit quantization for these models requires significantly higher memory than current high-end single-node EPYC configurations provide.
HOW THIS AFFECTS YOU
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builderYou may need to shift from local hosting to flash models or distributed inference to manage massive parameter counts.
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researcherHardware constraints for local training and inference are accelerating due to model size growth.