This article discusses the extensive evaluation of quantized large language models (LLMs) by Neural Magic, finding that quantized LLMs maintain competitive accuracy and efficiency with their full-precision counterparts.
- **Quantization Schemes**: Three different quantization schemes were tested: W8A8-INT, W8A8-FP, and W4A16-INT, each optimized for different hardware and deployment scenarios.
- **Accuracy Recovery**: The quantized models demonstrated high accuracy recovery, often reaching over 99%, across a range of benchmarks, including OpenLLM Leaderboard v1 and v2, Arena-Hard, and HumanEval.
- **Text Similarity**: Text generated by quantized models was found to be highly similar to that generated by full-precision models, maintaining semantic and structural consistency.
- **Quantization Schemes**: Three different quantization schemes were tested: W8A8-INT, W8A8-FP, and W4A16-INT, each optimized for different hardware and deployment scenarios.
- **Accuracy Recovery**: The quantized models demonstrated high accuracy recovery, often reaching over 99%, across a range of benchmarks, including OpenLLM Leaderboard v1 and v2, Arena-Hard, and HumanEval.
- **Text Similarity**: Text generated by quantized models was found to be highly similar to that generated by full-precision models, maintaining semantic and structural consistency.
