GSQ Quantization Explained with Qwen3.8 27B
Short for Gumbel-Softmax Quantization, it is used in models such as Qwen3.8-27B-GSQ-RCO-GGUF and Kimi-K2.5-2Bit-GSQ.
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Key points
- GSQ quantization is becoming increasingly visible among low-bit LLM releases.
- I use AutoRound for many of my quantizations, but lately I have also been using more GSQ models and making more of my own.
- The main motivation behind GSQ is straightforward: get better accuracy at low precision while keeping a simple scalar representation that existing inference kernels can use.
- In this article, I will explain how GSQ works, look at some implementation examples, and compare three quantized versions of Qwen3.8 27B with the BF16 reference.
Sources (1)
- [1]GSQ Quantization Explained with Qwen3.8 27BThe Kaitchup (Benjamin Marie) · Oct 22, 02:42 AM
Short for Gumbel-Softmax Quantization, it is used in models such as Qwen3.8-27B-GSQ-RCO-GGUF and Kimi-K2.5-2Bit-GSQ.
GSQ quantization is becoming increasingly visible among low-bit LLM releases.
Extractive summary: sentences quoted from the sources.
Before this
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