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Unsloth Runs DeepSeek V4.1 Flash Locally

Unsloth says DeepSeek V4.1-Flash-1017 runs locally, with 1-bit quantization retaining ~88.2% accuracy and shrinking size from 508GB to 250GB.

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Key points

  • Unsloth AI says DeepSeek V4.1-Flash-1017 can now run locally, with 1-bit quantization retaining about 88.2% accuracy while shrinking size from 508GB to 250GB, a 52% reduction.
  • It says the model runs on a 128GB+ RAM device and calls V4.1 Flash 1017 the strongest open model to date.
  • Daniel Han says the team worked on a llama.cpp implementation for DS4.1-Flash and made Unsloth dynamic GGUFs, finding Q6_K approximately equals MXFP8 while IQ4_NL loses a tiny bit of accuracy, and that the engram can stream fully from disk.
  • DeepSeek says DeepSeek-V4.1-Flash-1017 is live on its website, app and API, with stronger benchmark performance, faster task completion and shorter outputs than the previous version, and is optimized for DeepSeek Harness, Tencent CodeBuddy and WorkBuddy.

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Original post

Unsloth AI @UnslothAI 101.1K followers

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://t.co/YPEmDLas7d GGUF: https://t.co/1kD14VTAYP https://t.co/JyAwz8zOVOQuoting @deepseek_ai: 🚀 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, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4
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  1. Daniel Han @danielhanchen 55

    Unsloth adds llama.cpp support for DeepSeek V4.1-Flash and makes dynamic GGUFs, finding Q6_K matches MXFP8 and the engram can stream from disk.

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