Mixtral 8x22B Instruct v0.1 FP8 Model Overview Model Architecture: Mixtral 8x22B Instruct v0.1 Input: Text Output: Text Model Optimizations: Weight quantization: FP8 Activation quantization: FP8 Release Date: 2/26/2025 Version: 1.0 Model Developers: Neural Magic Quantized version of Mixtral 8x22B Instruct v0.1. It achieves an average score of 79.5 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 79.87. Model Optimizations This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized, except the MLP routers. Deployment Use with vLLM This model can be deployed efficiently using the vLLM backend, as shown in the example below. vLLM also supports OpenAI compatible serving. See the documentation for more details. Creation This model was created with llm compressor by running the code snippet below with the following command: Evaluation The model was evaluated on OpenL…
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