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"Ctranslate2" is an amazing library that runs these models. They are faster, more accurate, and use less VRAM/RAM than GGML and GPTQ models.

How to run with instructions: https://github.com/BBC-Esq

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Learn more about the amazing "ctranslate2" technology:"

Compatibility and Data Formats
Format Approximate Size Compared to float32 Nvidia GPU Required "Compute" Accuracy Summary
float32 100% 1.0 Offers more precision and a wider range. Most un-quantized models use this.
int16 51.37% 1.0 Same as int8 but with a larger range.
float16 50.00% 5.3 (e.g. Nvidia 10 Series and Higher) Suitable for scientific computations; balance between precision and memory.
bfloat16 50.00% 8.0 (e.g. Nvidia 30 Series and Higher) Often used in neural network training; larger exponent range than float16.
int8_float32 27.47% test manually (see below) Combines low precision integer with high precision float. Useful for mixed data.
int8_float16 26.10% test manually (see below) Combines low precision integer with medium precision float. Saves memory.
int8_bfloat16 26.10% test manually (see below) Combines low precision integer with reduced precision float. Efficient for neural nets.
int8 25% 1.0 Lower precision, suitable for whole numbers within a specific range. Often used where memory is crucial.
Web Link Description
CUDA GPUs Supported See what level of "compute" your Nvidia GPU supports.
CTranslate2 Quantization Even if your GPU/CPU doesn't support the data type of the model you download, "ctranslate2" will automatically run the model in a way that's compatible.
Bfloat16 Floating-Point Format Visualize data formats.
Nvidia Floating-Point Technical discussion.
Check Compatibility Manually Open a command prompt and run the following commands (may require CUDA toolkit and cuDNN installed as well, need to doublecheck this):
pip install ctranslate2
python
import ctranslate2

Check GPU/CUDA compatibility:

ctranslate2.get_supported_compute_types("cuda")

Check CPU compatibility:

ctranslate2.get_supported_compute_types("cpu")

It will print out your CPU/GPU compatibility. For example, a system with a 4090 GPU and 13900k would have the following compatibility:

CPU GPU
float32 โœ… โœ…
int16 โœ…
float16 โœ…
bfloat16 โœ…
int8_float32 โœ… โœ…
int8_float16 โœ…
int8_bfloat16 โœ…
int8 โœ… โœ…

Comparison of ctranslate2 and ggml

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