Super happy with the post and working with you, Damien, on this. Here's a quick tl;dr for anyone interested:
In this post, we shared how model quantization can dramatically cut AI model operational costs by reducing memory footprints **without sacrificing output quality** with the Llama 3 *B model.
Here’s what we did:
We converted a full-precision (FP16) Llama 3 8B model into a 4‑bit version using Hugging Face’s bitsandbytes library with the “nf4” configuration. This reduced the model’s memory usage from ~15 GB to ~5.4 GB—a savings of roughly 64%.
While the 4‑bit model experiences a 30–45% increase in inference time due to dequantization overhead, the output remains coherent and accurate. This approach makes deploying large models on resource-constrained hardware much more feasible.
Bonus: We give a full tutorial on Quantization, including how it works, the different techniques, and more.
Bonus 2: We have a detailed Google Colab for you in there!
Great article!
Super happy with the post and working with you, Damien, on this. Here's a quick tl;dr for anyone interested:
In this post, we shared how model quantization can dramatically cut AI model operational costs by reducing memory footprints **without sacrificing output quality** with the Llama 3 *B model.
Here’s what we did:
We converted a full-precision (FP16) Llama 3 8B model into a 4‑bit version using Hugging Face’s bitsandbytes library with the “nf4” configuration. This reduced the model’s memory usage from ~15 GB to ~5.4 GB—a savings of roughly 64%.
While the 4‑bit model experiences a 30–45% increase in inference time due to dequantization overhead, the output remains coherent and accurate. This approach makes deploying large models on resource-constrained hardware much more feasible.
Bonus: We give a full tutorial on Quantization, including how it works, the different techniques, and more.
Bonus 2: We have a detailed Google Colab for you in there!
Learn more in the iteration ;)
It was great working with you on this! Would love to explore more ideas together