🤯 This is absolutely insane! The local LLM landscape just changed completely!

Unsloth just released the latest Dynamic V3.1 quantization of DeepSeek-V4.1-Flash, and now you can take off with just 64GB of RAM! 🚀

Running a beast this size used to require expensive servers, but this release smashes the barrier to entry: 🔥 Put Engram directly on an SSD: blazing-fast, on-demand reads slash RAM usage! ⚡ Up to 3× faster inference: paired with DSpark acceleration, local inference feels unbelievably smooth! 🏆 Dominating benchmarks across the board: Dynamic V3.1 ranks first in nearly every benchmark among comparable quantizations! 📚 Tackles a massive 1M (1 million) context window: feed in an entire codebase or dozens of books in one go!

Devices with 64GB of RAM just became legendary. If you’ve got the hardware, jump in!👇

🔗 Model: https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF… 📖 Guide: https://unsloth.ai/docs/models/deepseek-v4.1-flash…

unsloth/DeepSeek-V4.1-Flash-1017-GGUF · Hugging Face

Source: https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF

https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF#read-our-how-to-run-deepseek-v41-flash-guideRead our How toRun DeepSeek-V4.1 Flash Guide! (https://unsloth.ai/docs/models/deepseek-v4.1-flash)

SeeUnsloth Dynamic 3.0 GGUFs (https://unsloth.ai/docs/basics/unsloth-dynamic-v3.0-gguf)for our quantization benchmarks.

• Run on a 128GB+ RAM device for the smallest 1-bit, 3-bit on 256GB or full precision on 320GB.Read Guide (https://unsloth.ai/docs/models/deepseek-v4.1-flash)
• To run, please use our llama.cpp PR or theUnsloth Desktop (https://unsloth.ai/docs/desktop)app.
• Get up to 3x faster inference with Unsloth vs other ngram/CPU/VRAM repos./

Quantization analysis, KLD plots and other analysisis here. (https://unsloth.ai/docs/models/deepseek-v4.1-flash) To swap the engram (MXFP8 to Q6_K / Q5_K / IQ4_NL, seethis guide (https://unsloth.ai/docs/models/deepseek-v4.1-flash#mix-n-matching-the-engram))

DeepSeek-V4.1

Homepage (https://www.deepseek.com/)Chat (https://chat.deepseek.com/)

Hugging Face (https://huggingface.co/deepseek-ai)Twitter Follow (https://twitter.com/deepseek_ai)

License (https://huggingface.co/unsloth/LICENSE)

Technical Report👁️ (https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/DeepSeek_V41_Tech_Report.pdf)

https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF#introductionIntroduction

DeepSeek-V4.1-Flash-1017is a minor update toDeepSeek-V4.1-Flash (https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash), delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete tasks faster and at lower cost and advancing the performance–cost Pareto frontier across multiple agent harnesses.

BenchmarkDS-V4.1-Flash-1017DS-V4.1-FlashK3GLM-5.3GPT-5.6 SolOpus 5HLE w/o tools42.6 (44.0†)36.8 (39.1†)43.542.0†44.556.3Terminal-Bench 4.042.331.212.637.939.951.8DeepSWE v1.177.074.267.566.973.074.0ProgramBench29.120.317.519.023.037.0NL2Repo-Bench68.265.458.058.056.875.3Automation-Bench v1.0.660.554.846.748.845.850.3Agents’ Last Exam35.431.827.628.526.728.6Apex Bench v1.172.164.647.856.654.365.8Chartography (w/tools)83.078.968.1—79.984.0BabyVision (w/tools)92.589.685.7—88.994.1ZeroBench-main (w/tools)51.049.041.0—53.052.0 † denotes the text-only subset of HLE. We use the Claude Code harness for Terminal-Bench 4.0 and the Mini-SWE-V2 harness for DeepSWE v1.1.

https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF#prompt-encodingPrompt Encoding

This release does not include a Jinja-format chat template. Theencoding (https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF/blob/main/encoding/README.md)folder contains a self-contained Python reference implementation (encoding\.py) with test cases for multi-turn conversations, tool calling, thinking mode, numeric reasoning effort, mid-conversation system messages, and interleaved image content.

For production use, we additionally releasedeepseek-recipe (https://github.com/deepseek-ai/deepseek-recipe), a set of Rust libraries with Python bindings that provides the same prompt format as a maintained, protocol-aware toolkit. It converts Messages, Chat Completions, and Responses API requests into the Conversation format, encodes them into DeepSeek V4 and V4.1 prompts or token IDs, and parses model output back into complete or streamed responses — covering thinking, tool calls, images, and generation settings. Model inference, tool execution, and HTTP transport are left to the caller.

https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF#minimal-inferenceMinimal Inference

Please refer to theinference (https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF/blob/main/inference/README.md)folder for instructions on weight conversion and running inference locally.

Recommended sampling parameters:

ParameterValuetemperature1.0top\_p1.0context\_window1M tokensmax\_tokens≥ 256K

https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF#licenseLicense

This repository and the model weights are licensed under theMIT License (https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF/tree/main/LICENSE).

https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF#citationCitation

@misc{deepseekai2026deepseekv41flash, title={DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression}, author={DeepSeek-AI}, year={2026}, }

https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF#contactContact

If you have any questions, please raise an issue or contact us at[email protected].