quantization

Quantization Techniques for LLM Inference: INT8, INT4, GPTQ, and AWQ

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Large language models have achieved remarkable capabilities, but their computational demands create a fundamental tension between performance and accessibility. A 70-billion parameter model in standard FP16 precision requires approximately 140GB of memory—far exceeding what’s available on consumer GPUs and even challenging high-end datacenter hardware. Quantization techniques address this challenge by reducing the numerical precision of … Read more

How to Quantize LLMs to 8-bit, 4-bit, 2-bit

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Model quantization has become essential for deploying large language models on consumer hardware, transforming models that would require enterprise GPUs into ones that run on laptops and mobile devices. By reducing the precision of model weights from 32-bit or 16-bit floating point numbers down to 8-bit, 4-bit, or even 2-bit integers, quantization dramatically decreases memory … Read more

How to Quantize LLM Models

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Large language models have become incredibly powerful, but their size presents a significant challenge. A model like Llama 2 70B requires approximately 140GB of memory in its full precision format, making it inaccessible to most individual developers and small organizations. Quantization offers a solution, compressing these models to a fraction of their original size while … Read more