Quantization Blog Posts

Future Of Extreme Quantization

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Guide To Quantizing With Bitsandbytes

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Quantization Deep Dive: How 4-bit and 1.5-bit Models Retain 99% of Their Original Accuracy

The immense power of Large Language Models (LLMs) comes with a significant burden: their colossal size. A 7-billion parameter model, stored in standard 16-bit floating-point precision (FP16), occupies 14 Gigabytes (GB) of memory. This is too large for many consumer GPUs, prohibitive for local deployment on laptops or edge devices, and costly for cloud inference. The problem is clear: to democratize access and enable ubiquitous AI, these models must become dramatically smaller, faster, and more energy-efficient without sacrificing their intelligence.