AI news · October 11, 2026
Saluki compresses Qwen3.8-27B into a 7.89GB tool-calling model
The Saluki model card lists a 7.89GB Qwen3.8 quant with 88/120 tool-call passes versus 84 for the full model, but also reports weaker math and formatting; test your own agent loop.
- listed GGUF file size
- 7.89GB
- reported tool-call score
- 88/120
- reported SWE-bench Verified score
- 30 vs 33
A post on r/LocalLLaMA from October 9 highlighted Saluki, an Apache 2.0 model distributed as a GGUF for llama.cpp. The model card describes it as a 7.89GB quant of Qwen3.8-27B, compared with about 54GB for the full model. It reports 88 of 120 tool-call tests versus 84 for the full model, 42 versus 35 parallel tool calls, and 30 versus 33 on SWE-bench Verified.
The card says Saluki retains about 96% of the source model's average result across nine benchmarks, but it also notes that the public numbers use different harnesses and that some parallel-call answers have formatting slips. A 0.6 to 0.9GB vision add-on is listed, and no inference provider is offered. The result is a local deployment option, not a guarantee of lower total cost.
This is exactly the type of model that needs a narrow local trial. Run the same tool calls through Saluki and the model you use now, then record VRAM use, parse failures, retries, and completion time. The model card is the source for these figures; no independent benchmark was found in the available material.
What you can do with it
Download the model card and run the same 20 tool calls on Saluki and your current local model. Check VRAM, parse errors, retries, and multi-step completion; the model is Apache 2.0 and works with llama.cpp.
Our take
A 7.89GB tool-capable model could make local agents easier to run. The benchmark table is encouraging but mixed, so the deciding evidence is behavior on your tools, prompts, and hardware.
Links Try it
Source: r/LocalLLaMA — Made With Models writes the brief; the reporting is theirs.