Quantization Explained: Run 70B Models on Consumer GPUs
SMRTR summary
Quantization techniques enable massive 70-billion parameter language models, which normally require 280GB of memory, to run on consumer GPUs with just 24GB by reducing numerical precision from 32-bit to 4-bit formats like GGUF and EXL2. These methods shrink memory requirements to around 38-40GB while maintaining quality within 2% of full precision models.
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