Hey everyone! We're Egzakta AI Lab, and I'm part of the team behind KVARK. We've just released our first open-weight model: KVARK Sparkle 1.0 (27B). It's based on Qwen3.8-27B, with a focus on making reasoning more efficient: fewer thinking tokens, faster responses, and comparable answer quality. The idea is simple: reasoning models can spend a lot of tokens thinking through tasks that don't necessarily require that much reasoning. We wanted to see how much of that overhead we could reduce without sacrificing performance, since fewer thinking tokens means less GPU time per answer and less waiting. Here's how Sparkle performed against the original Qwen3.8-27B: ~2× faster median response time: 13.7s vs. 28.1s. Up to 67% fewer thinking tokens. Comparable accuracy across five standard benchmarks: GPQA Diamond, AIME, LiveCodeBench, IFEval and MMLU-Pro. None of the accuracy differences were statistically significant in our testing. Also, we compared the models on practical tasks: Dashboard generation: 1.6× faster. Landing page generation: 1.4× faster. SVG illustration of a pelican riding a bicycle: 3.2× faster. You can find KVARK Sparkle 1.0 on Hugging Face: https://huggingface.co/Egzakta/Kvark-Sparkle-1.0-27B A few technical details for those interested in running it locally: Open weights under Apache 2.0. Available in BF16, FP8 and GGUF formats. Supports llama.cpp, vLLM, Transformers and SGLang. We'd love for you guys to give it a try! Let us know how it performs on your hardware, how it compares to the original Qwen, and what you think could be improved. Looking forward to your feedback!   submitted by   /u/dzulajla98 [link]   [comments]