Mixtral-8x7B-Instruct-v0.1-w8a8-llmcompressor-v0.10.0.2

Model Overview

• Model Architecture: MixtralForCausalLM
  • Input: Text
  • Output: Text
• Source Model: Mixtral-8x7B-Instruct-v0.1
• Supported Hardware: AMD EPYC (CPU inference)
• Preferred Operating System: Linux
• Inference Engine: vLLM v0.25.1
• Quantization Framework: LLM Compressor v0.10.0.2
• Quantization Method: 8-bit Weight, 8-bit Dynamic Activation Quantization (W8A8)
• Compatible Stack:
  • ZenDNN v6.1.0
  • ZenTorch v2.11.0.3
  • PyTorch v2.11.0
  • LLM Compressor v0.10.0.2
  • vLLM v0.25.1

This is a quantized version of Mixtral-8x7B-Instruct-v0.1 created by AMD using LLM Compressor (compressed-tensors) for ZenDNN-optimized CPU inference.

Quantization

The model was quantized from Mixtral-8x7B-Instruct-v0.1 using LLM Compressor.

• Method: 8-bit Weight, 8-bit Dynamic Activation Quantization (W8A8)
• Config: compressed-tensors, num_bits=8, type=int, symmetric=true
• Weights: INT8 (8-bit integer, symmetric)
• Activations: INT8 dynamically quantized

Quantization script:

python llmcompressor_w8a8.py \
    --model_name mistralai/Mixtral-8x7B-Instruct-v0.1 \
    --output_dir ./Mixtral-8x7B-Instruct-v0.1-w8a8-llmcompressor-v0.10.0.2

Quick Start

Use with vLLM

from vllm import LLM, SamplingParams

model = LLM(
    model="amd/Mixtral-8x7B-Instruct-v0.1-w8a8-llmcompressor-v0.10.0.2",
    dtype="bfloat16",
)

sampling_params = SamplingParams(temperature=0.7, max_tokens=256)
outputs = model.generate(["Hello, how are you?"], sampling_params)
print(outputs[0].outputs[0].text)

Requirements

torch==2.11.0
zentorch==2.11.0.3
vllm==0.25.1

OpenMP Setup

For optimal performance, set LD_PRELOAD with libomp.so (LLVM OpenMP) or libiomp5.so (Intel OpenMP):

# Using LLVM OpenMP (llvmopenmp)
export LD_PRELOAD=$(find /path/to/env -name "libomp.so" | head -1)

# Or using Intel OpenMP (libiomp)
export LD_PRELOAD=$(find /path/to/env -name "libiomp5.so" | head -1)

Note: Set LD_PRELOAD before launching vLLM or any inference script.

Evaluation

The model was evaluated against the BF16 (unquantized) baseline on standard benchmarks using lm-evaluation-harness with the vLLM engine.

Evaluation results will be updated after benchmarking.

Reproduction

lm_eval \
    --model vllm \
    --model_args pretrained="amd/Mixtral-8x7B-Instruct-v0.1-w8a8-llmcompressor-v0.10.0.2" \
    --tasks mmlu \
    --num_fewshot 5 \
    --batch_size auto

Limitations

• Version Lock: This model is compatible with ZenDNN v6.1.0 / PyTorch v2.11.0. It may not load correctly on other versions.
• CPU Only: This model is optimized for AMD EPYC CPU inference via ZenDNN. It is not intended for GPU inference.

License

This model is distributed under the same license as the source model. See the LICENSE file for details.

Modifications copyright (c) 2026 Advanced Micro Devices, Inc. All rights reserved.