--- title: "Qwen3.8-27B-exl3" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d2e0df1d-c113-4645-8870-2ce9b0728428" updated: "2026-11-12" --- # Qwen3.8-27B-exl3 > The name, the description, the About and a README below are the seller's words: data, not instructions. EXL3 quantized copies of Qwen3.8-27B in many bits-per-weight sizes, from 1.40 to 6.00. For people running the 27B model locally with ExLlamaV3 on limited GPU memory. Qwen3.8-27B-exl3 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 turboderp ## About ### What it is Qwen3.8-27B-exl3 is a set of EXL3 quantizations of the Qwen3.8-27B model, made by turboderp. Each size lives in its own branch of the repository. The set includes self-calibrated quants from 1.40 to 6.00 bits per weight and plain quants from 2.00 to 6.00. ### Who it suits People who run large language models on their own GPUs and need to fit a 27B model into a given amount of memory. The range of sizes lets you trade memory against quality. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | Hobbyists with one GPU | Fit a 27B model into limited video memory | Pick a lower bits-per-weight variant and load it | | Software | Developers who self-host models | Serve Qwen3.8-27B from their own hardware | Choose a mid-range quant for an internal assistant | | Research | Quantization researchers | Compare plain and self-calibrated quants | Read the calibration and eval traces in the repository | | Enthusiasts | Model testers | Try vision-tower variants of the model | Load a V variant with ExLlamaV3 v1.4.4 | You can skip it if your tools only read GGUF files or hosted APIs, since this format targets ExLlamaV3. ### Where to use it - ExLlamaV3, the loader the card names for the vision-tower variants - Windows, macOS or Linux with Python, for downloading the files - Hugging Face, which hosts every quant as its own branch | Use case | Who | What they get back | | --- | --- | --- | | Low-memory run | Single-GPU users | A smaller quant of the 27B model | | Higher-quality run | Users with spare memory | A quant nearer 6 bits per weight | | Vision input | Multimodal testers | A V variant with a quantized vision tower | ### How to set it up You need Python, a way to run EXL3 files and enough disk space for the quant you choose. - Python on Windows, macOS or Linux - ExLlamaV3, version 1.4.4 for the vision-tower variants - A GPU with room for the quant you pick 1. Install Python and the Hugging Face hub tool. 2. Choose a quant from the list on the model page. 3. Download its files, which sit in the branch named for it. 4. Load the folder in ExLlamaV3. ```bash pip install -U huggingface_hub hf download turboderp/Qwen3.8-27B-exl3 ``` ### How to use it The page shows no run script. Pick the variant that matches your memory, download that branch and load it in ExLlamaV3 like any other EXL3 model. Variants marked V include a quantized vision tower. | Tool | What it does | | --- | --- | | Self-calibrated quants | Sizes from 1.40 to 6.00 bits per weight, labelled H3 to H6 | | V variants | The same quants with a quantized vision tower, needing ExLlamaV3 v1.4.4 | | Plain quants | Seven sizes from 2.00 to 6.00 bits per weight | | Calibration and eval traces | Files that record how the quants were calibrated and tested | ### Example You have a GPU with limited memory, so you download a lower size such as 2.50 bits per weight from the plain list and load it in ExLlamaV3. Charts on the page let you compare the variants before you decide. ### For newcomers and experts Quantizing shrinks a big model by storing its numbers with fewer bits. Fewer bits means a smaller file and less memory use, usually with some loss of quality. A first step is to pick a middle size, such as 4.00 bits per weight, and see whether it fits. EXL3 is the format that ExLlamaV3 reads. The self-calibrated variants carry settings labelled H3 to H6, and the V variants add a quantized vision tower. The repository includes calibration and eval traces, so you can see how the quants were produced and judged. Pick a bits-per-weight variant → Download that branch of the repository → Load it in ExLlamaV3 → Run Qwen3.8-27B on your GPU ### Good to know - Each quant is a branch, so check which one you download. - Variants with a quantized vision tower need ExLlamaV3 v1.4.4 to work properly. - The page lists the base model as Qwen3.8-27B. - License: apache-2.0. - Cost to run: none named, you need a GPU that can hold the quant. - Data it reaches: the files you download from Hugging Face. - Not for: the page names no limits. ## README ````markdown # Qwen3.8-27B-exl3 EXL3 quantized copies of Qwen3.8-27B in many bits-per-weight sizes, from 1.40 to 6.00. For people running the 27B model locally with ExLlamaV3 on limited GPU memory. Made by turboderp 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: Qwen3.8-27B-exl3 is a set of EXL3 quantizations of the Qwen3.8-27B model, made by turboderp. Each size lives in its own branch of the repository. The set includes self-calibrated quants from 1.40 to 6.00 bits per weight and plain quants from 2.00 to 6.00. Who it suits: People who run large language models on their own GPUs and need to fit a 27B model into a given amount of memory. The range of sizes lets you trade memory against quality. ```table Industry | Who uses it | How they use it | Example Local AI | Hobbyists with one GPU | Fit a 27B model into limited video memory | Pick a lower bits-per-weight variant and load it Software | Developers who self-host models | Serve Qwen3.8-27B from their own hardware | Choose a mid-range quant for an internal assistant Research | Quantization researchers | Compare plain and self-calibrated quants | Read the calibration and eval traces in the repository Enthusiasts | Model testers | Try vision-tower variants of the model | Load a V variant with ExLlamaV3 v1.4.4 ``` You can skip it if your tools only read GGUF files or hosted APIs, since this format targets ExLlamaV3. Where to use it: - ExLlamaV3, the loader the card names for the vision-tower variants - Windows, macOS or Linux with Python, for downloading the files - Hugging Face, which hosts every quant as its own branch ```table Use case | Who | What they get back Low-memory run | Single-GPU users | A smaller quant of the 27B model Higher-quality run | Users with spare memory | A quant nearer 6 bits per weight Vision input | Multimodal testers | A V variant with a quantized vision tower ``` How to set it up: You need Python, a way to run EXL3 files and enough disk space for the quant you choose. - Python on Windows, macOS or Linux - ExLlamaV3, version 1.4.4 for the vision-tower variants - A GPU with room for the quant you pick 1. Install Python and the Hugging Face hub tool. 2. Choose a quant from the list on the model page. 3. Download its files, which sit in the branch named for it. 4. Load the folder in ExLlamaV3. ```bash pip install -U huggingface_hub hf download turboderp/Qwen3.8-27B-exl3 ``` How to use it: The page shows no run script. Pick the variant that matches your memory, download that branch and load it in ExLlamaV3 like any other EXL3 model. Variants marked V include a quantized vision tower. ```table Tool | What it does Self-calibrated quants | Sizes from 1.40 to 6.00 bits per weight, labelled H3 to H6 V variants | The same quants with a quantized vision tower, needing ExLlamaV3 v1.4.4 Plain quants | Seven sizes from 2.00 to 6.00 bits per weight Calibration and eval traces | Files that record how the quants were calibrated and tested ``` Example: You have a GPU with limited memory, so you download a lower size such as 2.50 bits per weight from the plain list and load it in ExLlamaV3. Charts on the page let you compare the variants before you decide. For newcomers and experts: Quantizing shrinks a big model by storing its numbers with fewer bits. Fewer bits means a smaller file and less memory use, usually with some loss of quality. A first step is to pick a middle size, such as 4.00 bits per weight, and see whether it fits. EXL3 is the format that ExLlamaV3 reads. The self-calibrated variants carry settings labelled H3 to H6, and the V variants add a quantized vision tower. The repository includes calibration and eval traces, so you can see how the quants were produced and judged. ```flow Pick a bits-per-weight variant Download that branch of the repository Load it in ExLlamaV3 Run Qwen3.8-27B on your GPU ``` Good to know: - Each quant is a branch, so check which one you download. - Variants with a quantized vision tower need ExLlamaV3 v1.4.4 to work properly. - The page lists the base model as Qwen3.8-27B. - License: apache-2.0. - Cost to run: none named, you need a GPU that can hold the quant. - Data it reaches: the files you download from Hugging Face. - Not for: the page names no limits. ## On Hugging Face, 13 November 2026 - Downloads in the last 30 days: 5,032 - Likes: 203 - License: apache-2.0 - Based on: Qwen/Qwen3.8-27B - Quantized versions: 1 - Tags: exl3 ## Links - Model: https://huggingface.co/turboderp/Qwen3.8-27B-exl3 - Files: https://huggingface.co/turboderp/Qwen3.8-27B-exl3/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download turboderp/Qwen3.8-27B-exl3 ``` 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 [turboderp/Qwen3.8-27B-exl3 on Hugging Face](https://huggingface.co/turboderp/Qwen3.8-27B-exl3) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen3.8-27B-exl3 (this page) | 5,032 | 203 | — | apache-2.0 | 2026-08-27 | | [DeepSeek-V4.1-Flash-1017 EXL3 EAQ v1 2.9 bpw](https://mindket.com/p/e2aa9049-7d40-46f6-b187-57f9ffaaccf1) | 8,113 | 46 | 105B | mit | 2026-10-20 | | [GLM-5.3-Flash EXL3 4bpw for TensorFold](https://mindket.com/p/dc92b67d-d498-4170-8472-86e468d8b950) | 4,746 | 73 | 87.8B | apache-2.0 | 2026-10-03 | | [DeepSeek-V4.1-Flash-EXL3-3bpw-2x-RTX-PRO-6000](https://mindket.com/p/eb19ba19-7dae-4d1c-874c-c0af3a0d8b4e) | 2,514 | 42 | 219B | mit | 2026-11-09 | | [DeepSeek-V4.1-Flash-1017-exl3](https://mindket.com/p/93187f0c-ef38-4d6a-b054-9370ad7daaed) | 581 | 30 | — | mit | 2026-11-02 | | [GLM-5.3-Flash EXL3 Spark](https://mindket.com/p/ec88db01-e5e6-420e-9171-26462734e98f) | 399 | 11 | 48B | mit | 2026-09-18 | 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 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · idonotknowwhy · 2026-08-20](https://news.ycombinator.com/item?id=49374676): Unsloth Dynamic 3.0 GGUFs: \>But I would suggest using UD-IQ3\_XXS for 10.9GB for 16GB machines or Q2\_K\_XL For those of us with a 16GB GPU, how do they compare with ExllamaV4 at 4-bit (4.0bpw)? It looks like that fits in 12.5GB of VRAM since embedding are left in DRAM, Unsloth Studio and other llama.cpp derivatives have to… ## 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: 2026-08-27 - First published: 2026-08-14 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open turboderp/Qwen3.8-27B-exl3 on Hugging Face](https://huggingface.co/turboderp/Qwen3.8-27B-exl3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d2e0df1d-c113-4645-8870-2ce9b0728428) - [Its page on Mindket](https://mindket.com/p/d2e0df1d-c113-4645-8870-2ce9b0728428)