Underdog compresses Qwen3.8-27B to 7.89GB with improved tool-calling performance

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Release Time: 2026/10/08 07:15:21

Summary

Underdog, a local AI assistant backed by a16z, has launched Saluki 27B, a 2-bit quantized version of Qwen3.8-27B. The model size has been reduced from 54GB to 7.89GB, enabling it to run on laptops with 16GB of RAM. Saluki solved 88 out of 120 tool-calling questions, outperforming ISTA’s 76 and the original’s 84. It solved 30 out of 50 GitHub issues, slightly behind the original’s 33. The model retains 96% of the original’s average performance across nine benchmarks, but scores 83% in math and 85% in complex reasoning. This AI and crypto news highlights the on-chain potential of lightweight, high-performance models.

ME AI message: Underdog, a local AI assistant backed by a16z, has released Saluki 27B, built on Alibaba’s Qwen3.8-27B. The model uses approximately 2-bit quantization, reducing its size from about 54GB in full precision to 7.89GB. According to the official announcement, Saluki can run on a laptop with just 16GB of RAM and supports deployment via existing tools like llama.cpp. The model weights have been released under the Apache 2.0 license. Saluki leverages quantization research from ISTA-DASLab at the Institute of Science and Technology Austria. Previously, this lab had compressed Qwen3.8-27B to approximately 8.4GB. Underdog further reduced the size and focused on optimizing tool-use performance. In a benchmark of 120 tool-use questions selected from BFCL, Saluki correctly answered 88, the ISTA-quantized version answered 76, and the original 54GB model answered 84. In 50 real-world GitHub issue-fixing tasks, Saluki resolved 30, while the original model resolved 33. The official team states that Saluki retains an average of 96% of the original model’s performance across nine evaluations, though performance varies significantly across tasks. Scores in math competitions and complex reasoning tasks reach only about 83% and 85% of the original model’s performance, respectively. (Source: BlockBeats)

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