Unverified50% confidenceFactExact time
Open-source weights mean the model parameter files can be downloaded and run on your own; distillation is using a large model to generate data to train a model with far fewer parameters, and distilled small models commonly come in sizes of 7B, 14B and 32B, and after quantization and compression can run on a single consumer-grade graphics card or even purely on CPU.
1
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50%
Confidence
Long-term
Relevance
10/13/2026
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UnverifiedA 7B-parameter model requires approximately 14 GB of VRAM for model weights alone in FP16 format, and the total VRAM requirement for training may balloon to 50–80 GB.74% similarUnverifiedOn-device large models compress parameter counts to the 1B–7B level through techniques such as model quantization (compressing FP32 to INT4/INT8), knowledge distillation, and structural pruning, enabling real-time inference on local chips.72% similarUnverifiedA 125-billion-parameter model can run on 12G of VRAM, relying mainly on model quantization and a mixture-of-experts (MoE) architecture, achieved by activating only part of the expert parameters, quantizing weights to 4bit or lower, and dynamically loading inactive parameters from memory or SSD on demand.72% similarUnverifiedPost-training quantization methods such as GPTQ and AWQ can compress 7B-parameter models to a size that can run on consumer-grade GPUs.70% similar
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