---
inference: false
base_model: TheDrummer/Behemoth-R1-123B-v2
base_model_relation: quantized
tags:
  - exl2
library_name: exllamav2
pipeline_tag: text-generation
---

exllamav2 quantizations of TheDrummer's [Behemoth-R1-123B-v2](https://huggingface.co/TheDrummer/Behemoth-R1-123B-v2)

[2.25bpw h6](https://huggingface.co/MikeRoz/Behemoth-R1-123B-v2-exl2/tree/2.25bpw_H6) (32.964 GiB)    
[4.25bpw h6](https://huggingface.co/MikeRoz/Behemoth-R1-123B-v2-exl2/tree/4.25bpw_H6) (61.324 GiB)    
[5.00bpw h6](https://huggingface.co/MikeRoz/Behemoth-R1-123B-v2-exl2/tree/5.00bpw_H6) (71.959 GiB)    
[8.00bpw h8](https://huggingface.co/MikeRoz/Behemoth-R1-123B-v2-exl2/tree/8.00bpw_H8) (114.559 GiB)     
[measurement.json](https://huggingface.co/MikeRoz/Behemoth-R1-123B-v2-exl2/resolve/main/measurement.json?download=true)  

The 2.25bpw quant will load with 28k fp16 context on 2 24 GB GPUs, or 89k fp16 context on 3 24 GB GPUs.    
The 4.25bpw quant will squeeze into 3 24GB GPUs with 16k fp16 context, but can load with 73k of fp16 context in 4 24GB GPUs.    
The 8.00bpw quant requires 6 24 GB GPUs (or equivalent)