Unsloth releases 1-bit GGUF builds for DeepSeek V4.1-Flash-1017
Unsloth released GGUF builds of DeepSeek V4.1-Flash-1017 for local use, including a 1-bit configuration that it says retains about 88.2% accuracy while reducing model size from 508GB to 250GB. The company also claims strong benchmark rankings, but the reports provide no independent measurements, and the stated RAM requirement varies between 64GB and 128GB across its posts.
The release makes DeepSeek V4.1-Flash-1017 available for local deployment in a substantially smaller format, though the accuracy, benchmark and hardware claims remain vendor-provided.
DeepSeek V4.1-Flash-1017 can now run locally! 🐳 We made 1-bit retain ~88.2% accuracy while shrinking size from 508GB to 250GB (−52%). Run on a 128GB+ RAM device. V4.1 Flash 1017 is the strongest open model to date Guide: https://unsloth.ai/docs/models/deepseek-v4.1-flash GGUF: https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF
DeepSeek: 🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy,
#DeepSeek V4.1-Flash-1017#Unsloth#DeepSeek#Dynamic V3.1#DeepSeek Harness#Tencent CodeBuddy