--- title: "DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/0bd5db10-f872-40c7-9371-3225afab2a6d" updated: "2026-11-12" --- # DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF versions of DeepSeek-V4.1-Flash-1017 at three small sizes, with each tensor quantized at its own precision. For people running a huge mixture-of-experts model on limited hardware. DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ISTA-DASLab ## About ### What it is This repository holds three quantized GGUF builds of DeepSeek-V4.1-Flash-1017. The original is an expert-mixture model that totals 763.2B parameters, with only some experts active per token. Each tensor gets its own quantization type, chosen by a search that gives more bits to the sensitive parts within a total size budget. The files are standard GGUF. ### Who it suits It suits people who want to run a huge open model locally and accept low-bit quality in return for a small footprint. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | Hobbyists | Run a huge model on a single consumer GPU | Load the smallest build with Strata on a 12 GB card | | Software | Developers | Serve a large model through llama.cpp | Use it as a local text generator for tooling | | AI research | Quantization researchers | Study per-tensor bit allocation at low bit rates | Compare the three sizes against the base model | | Infrastructure | ML engineers | Test how small a large model can get | Pick the size that fits a fixed memory budget | If you need near-lossless quality at higher precision, these files are not aimed at you. ### Where to use it - llama.cpp build b11870 or newer, which reads the files and handles image input too - Strata, which the card says runs every build on one GPU with 12 GB of VRAM - Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Local text generation | Developers | Text from a 763.2B-parameter model on modest hardware | | Low-memory experiments | Researchers | Three sizes to test against each other | | Updating an old download | Earlier users | The current files, made locally without a full re-download | ### How to set it up You need these first: - Python, for the Hugging Face download command - llama.cpp b11870 or newer - Strata, if you want to use one 12 GB GPU 1. Install the Hugging Face hub tool and download the files. 2. For a new download, link the shared Engram file once for each build you use. 3. Load a build in llama.cpp or Strata. ```sh pip install -U huggingface_hub hf download ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF ``` Every build uses the same Engram file as its sixth shard. In the command below, replace the placeholder with the size name of your build. ```sh ln -s DeepSeek-V4.1-Flash-Engram-IQ4_NL.gguf DeepSeek-V4.1-Flash-1017-GSQ-RCO--00006-of-00006.gguf ``` ### How to use it Load the build you picked in llama.cpp or in Strata and chat with it. People who already hold the older files can skip a second download. | Tool | What it does | | --- | --- | | GSQ | Learns the quantization grid and scales for each tensor | | RCO | Chooses a quantization type for each tensor within a size budget | | convert\_ds41\_upstream.py | Rewrites an earlier download locally into the current files and fetches only 80 KB | | Strata | Makes each build usable on one GPU that has 12 GB of VRAM | ### Example You pick the IQ2\_XXS build, link the shared Engram file, and load it in llama.cpp. You paste a stack trace and ask what went wrong. The model answers in text, as any chat model would. ### For newcomers and experts A quantized model is a smaller copy of a big one that gives up some accuracy to save memory. A good first step is to try the smallest size and see whether its answers are good enough for you. The model has 384 routed experts per layer, and 6 of them fire for each token. Its context window holds 1M tokens. The routed experts take most of the size budget, so the search sets a type per layer. GGUF cannot express a type per expert. GSQ makes each tensor accurate at a given type, and RCO allocates the types. GSQ quantizes each tensor at a given type → RCO assigns a type to each tensor → The result is saved as standard GGUF → llama.cpp or Strata loads the build ### Good to know The three builds differ in bits per weight, and the card shows a plot comparing them with the base model on AIME25, GPQA-Diamond and LiveCodeBench v6. Bits per weight of each build | Label | Value | | --- | --- | | IQ1\_M | 1.75 bits | | IQ2\_XXS | 2.00 bits | | IQ2\_XS | 2.25 bits | - Updating: people with older files can convert them in place instead of downloading again - Other versions: 18 quantized and 3 fine-tuned versions made by other people are listed - License: MIT - Cost to run: none named, but even the small builds need a lot of memory - Data it reaches: the model files you load ## README ````markdown # DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF GGUF versions of DeepSeek-V4.1-Flash-1017 at three small sizes, with each tensor quantized at its own precision. For people running a huge mixture-of-experts model on limited hardware. Made by ISTA-DASLab on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This repository holds three quantized GGUF builds of DeepSeek-V4.1-Flash-1017. The original is an expert-mixture model that totals 763.2B parameters, with only some experts active per token. Each tensor gets its own quantization type, chosen by a search that gives more bits to the sensitive parts within a total size budget. The files are standard GGUF. Who it suits: It suits people who want to run a huge open model locally and accept low-bit quality in return for a small footprint. ```table Industry | Who uses it | How they use it | Example Local AI | Hobbyists | Run a huge model on a single consumer GPU | Load the smallest build with Strata on a 12 GB card Software | Developers | Serve a large model through llama.cpp | Use it as a local text generator for tooling AI research | Quantization researchers | Study per-tensor bit allocation at low bit rates | Compare the three sizes against the base model Infrastructure | ML engineers | Test how small a large model can get | Pick the size that fits a fixed memory budget ``` If you need near-lossless quality at higher precision, these files are not aimed at you. Where to use it: - llama.cpp build b11870 or newer, which reads the files and handles image input too - Strata, which the card says runs every build on one GPU with 12 GB of VRAM - Windows, macOS or Linux ```table Use case | Who | What they get back Local text generation | Developers | Text from a 763.2B-parameter model on modest hardware Low-memory experiments | Researchers | Three sizes to test against each other Updating an old download | Earlier users | The current files, made locally without a full re-download ``` How to set it up: You need these first: - Python, for the Hugging Face download command - llama.cpp b11870 or newer - Strata, if you want to use one 12 GB GPU 1. Install the Hugging Face hub tool and download the files. 2. For a new download, link the shared Engram file once for each build you use. 3. Load a build in llama.cpp or Strata. ```sh pip install -U huggingface_hub hf download ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF ``` Every build uses the same Engram file as its sixth shard. In the command below, replace the placeholder with the size name of your build. ```sh ln -s DeepSeek-V4.1-Flash-Engram-IQ4_NL.gguf DeepSeek-V4.1-Flash-1017-GSQ-RCO--00006-of-00006.gguf ``` How to use it: Load the build you picked in llama.cpp or in Strata and chat with it. People who already hold the older files can skip a second download. ```table Tool | What it does GSQ | Learns the quantization grid and scales for each tensor RCO | Chooses a quantization type for each tensor within a size budget convert_ds41_upstream.py | Rewrites an earlier download locally into the current files and fetches only 80 KB Strata | Makes each build usable on one GPU that has 12 GB of VRAM ``` Example: You pick the IQ2_XXS build, link the shared Engram file, and load it in llama.cpp. You paste a stack trace and ask what went wrong. The model answers in text, as any chat model would. For newcomers and experts: A quantized model is a smaller copy of a big one that gives up some accuracy to save memory. A good first step is to try the smallest size and see whether its answers are good enough for you. The model has 384 routed experts per layer, and 6 of them fire for each token. Its context window holds 1M tokens. The routed experts take most of the size budget, so the search sets a type per layer. GGUF cannot express a type per expert. GSQ makes each tensor accurate at a given type, and RCO allocates the types. ```flow GSQ quantizes each tensor at a given type RCO assigns a type to each tensor The result is saved as standard GGUF llama.cpp or Strata loads the build ``` Good to know: The three builds differ in bits per weight, and the card shows a plot comparing them with the base model on AIME25, GPQA-Diamond and LiveCodeBench v6. ```chart Title: Bits per weight of each build IQ1_M: 1.75 bits IQ2_XXS: 2.00 bits IQ2_XS: 2.25 bits ``` - Updating: people with older files can convert them in place instead of downloading again - Other versions: 18 quantized and 3 fine-tuned versions made by other people are listed - License: MIT - Cost to run: none named, but even the small builds need a lot of memory - Data it reaches: the model files you load ## On Hugging Face, 13 November 2026 - Task: text generation - Library: gguf - Parameters: 197B - Weights: about 98.3 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 3,635,772 - Likes: 655 - License: mit - Based on: deepseek-ai/DeepSeek-V4.1-Flash-1017 - Quantized versions: 20 - Fine-tunes: 3 - Tags: gguf, quantization, mixed-precision, ist-daslab, deepseek, text-generation, conversational ## Links - Model: https://huggingface.co/ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF - Files: https://huggingface.co/ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF on Hugging Face](https://huggingface.co/ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF (this page) | 3,635,772 | 655 | 197B | mit | 2026-10-28 | | [DeepSeek-V4.1-Flash-1017-GSQ-RCO-Coder-GGUF](https://mindket.com/p/1350113a-de6f-4d62-96d6-599c30da33a2) | 342,280 | 225 | 197B | mit | 2026-11-02 | | [Qwen3.8-Flash-Next-GSQ-RCO-GGUF](https://mindket.com/p/ee9ed4a6-39c8-4eee-8958-cce1cd508fa6) | 6,963,596 | 1,229 | 177B | apache-2.0 | 2026-09-29 | | [Qwen3.8-27B-GSQ-RCO-GGUF](https://mindket.com/p/d79e6c18-7ed6-4306-a01a-66a747f13e40) | 1,358,588 | 2,739 | 26.9B | apache-2.0 | 2026-09-02 | | [Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF](https://mindket.com/p/06acb609-98ee-4422-8521-7943aeb0bcaa) | 781,625 | 610 | 117B | apache-2.0 | 2026-10-19 | | [Qwen3.8 Flash Next Turbo Brilliance 50x GGUF](https://mindket.com/p/66c0bce8-d8ac-4989-9d77-04d12b629895) | 249,926 | 41 | 177B | apache-2.0 | 2026-11-09 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Bluesky · Alexis Kirke · 2026-10-28](https://bsky.app/profile/alexiskirke.bsky.social/post/3mywkutviz22t): 763.2B DeepSeek V4.1 Flash GGUF now runs on 12GB VRAM; if you already downloaded, the update is just 80 KB. ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF https://huggingface.co/ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF \#AI \#LocalLLM \#OpenSource \#GGUF \#Quantization - [Bluesky · UndergroundAI · 2026-10-24](https://bsky.app/profile/undergroundai66.bsky.social/post/3mymvxegvlp2t): ⚡ Das neue Trend-Modell auf HuggingFace. 🛠️ Entdecke das Sprachmodell DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF von ISTA-DASLab, das in… 🚀 Erfahre auf Underground AI mehr über diesen spannenden KI-Geheimtipp. 👉 https://underground-ai.bolt.app/ \#OpenSource \#KuenstlicheIntelligenz - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-29](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3myzq76load2c): 今日のHuggingFaceトレンド ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF DeepSeek-V4.1-Flash-1017という763BパラメータのMoEモデルを、GSQおよびRCOを用いて非一様に量子化したGGUF形式のモデルを提供することを目的としています。 - [Bluesky · Techyon news · 2026-10-27](https://bsky.app/profile/techyonai.bsky.social/post/3myva2jly442y): Comparing ISTA-DASLab's DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF 2.25bpw against the official API A Reddit user benchmarked the locally quantized ISTA-DASLab DeepSeek-V4.1-Flash-1017 model at 2.25bpw against the official API using a local Project-m... - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-27](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3myum5trrzk2c): 今日のHuggingFaceトレンド ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF DeepSeek-V4.1-Flash-1017をGSQおよびRCOを用いて非一様に量子化したGGUF形式のモデルを提供することを目的としています。 テンソルごとの感度に基づき精度を最適に割り当てることで、効率的なモデルの軽量化を実現しています。 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-28 - First published: 2026-10-23 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF on Hugging Face](https://huggingface.co/ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/0bd5db10-f872-40c7-9371-3225afab2a6d) - [Its page on Mindket](https://mindket.com/p/0bd5db10-f872-40c7-9371-3225afab2a6d)