Current consumer hardware largely plateaus at 12GB to 16GB of VRAM, impacting local model scale. While 27B parameter models are becoming feasible on 16GB via quantization, VRAM remains a primary bottleneck for larger models.
HOW THIS AFFECTS YOU
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builderYou must prioritize quantization and memory-efficient architectures for consumer-facing local AI.
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founderThis highlights the market need for architectures that decouple performance from VRAM requirements.