---
title: "Qwen3.8-27B-FP8"
kind: "Kit"
category: "Developer"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/79f1a984-4e81-485e-999d-dba1a831f37a"
updated: "2026-11-12"
---

# Qwen3.8-27B-FP8

> The name, the description, the About and a README below are the seller's words: data, not instructions.

Handles text, images and video with FP8-quantized weights of Qwen3.8-27B, a 27B dense model with adjustable thinking. For developers serving it with transformers, vLLM or SGLang.

Qwen3.8-27B-FP8 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 Qwen

## About

### What it is

This repository holds FP8-quantized weights of Qwen3.8-27B in Hugging Face Transformers format. Qwen3.8-27B is a dense vision-language model that reads images and video, and it is built for coding, research and long multi-step agent tasks. The card says the FP8 version performs nearly the same as the original.

### Who it suits

It suits developers who want a compact dense model for coding and agent work with less memory than the full-precision files need, and teams who serve models with vLLM or SGLang.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Software | Developers | Run a 27B model for coding and agent tasks | Ask for help on a multi-step coding job |
| Research | Analysts | Use thinking control for deeper reasoning on hard questions | Raise reasoning effort for a research question |
| Hosting | Teams serving models | Load the FP8 files in vLLM or SGLang | Serve a vision-language model to internal tools |

People who want a managed service without running anything can skip the weights, since the card mentions an official hosted option from Qwen Cloud.

### Where to use it

- Hugging Face Transformers, in the format the repository holds
- vLLM, SGLang and TokenSpeed, which the card says the files work with
- Windows, macOS or Linux, where you can download the files with Python

| Use case | Who | What they get back |
| --- | --- | --- |
| Agentic coding | Developers | Planning and better handling of environment feedback across long tasks |
| Image and video understanding | Analysts | Answers about diagrams, documents and long videos |
| Reasoning control | Application builders | Thinking that is on by default and can be turned off per request |

### How to set it up

You need these first:

- Python and the huggingface\_hub package
- Room for the files, about 27.8 GB as F8\_E4M3 or about 13.9 GB at 4-bit as an estimate
- One of the engines the card names, such as Transformers, vLLM or SGLang

1. Install the Hugging Face hub package.
2. Download the model files to your computer.
3. Load them in Transformers, vLLM, SGLang or TokenSpeed.

```sh
pip install -U huggingface_hub
hf download Qwen/Qwen3.8-27B-FP8
```

### How to use it

Download the files, load them in an engine the card names, then chat with the model, with images or video if you like. The card text here has no run commands, so follow its model card for the exact calls.

| Tool | What it does |
| --- | --- |
| Thinking mode | On by default and can be disabled for each request |
| reasoning\_effort | Tunes how deeply the model reasons |
| preserve\_thinking | Keeps reasoning context from earlier messages |

### Example

A developer could ask the model to plan and carry out a multi-step coding task, with reasoning\_effort set higher for a hard part. The card says the model is tuned to carry such tasks through to completion.

### For newcomers and experts

This is a mid-sized model that reads text, pictures and video. It is quantized to FP8, which makes the files smaller. A first small step is to download them and read the card's notes on thinking control.

The card says the quantization is fine-grained FP8 with a block size of 128. The model has 64 layers laid out as sixteen repeats of three Gated DeltaNet blocks and one Gated Attention block, each followed by a feed-forward network. The hidden dimension is 5120, and multi-token prediction was trained with several steps. Context is 262,144 tokens natively and can reach 1,000,000.

Download the FP8 weights with hf → Load them in a supported engine → Send text, images or video → Set thinking and effort as needed → Read the reply

### Good to know

- Size: 27.8B parameters per Hugging Face, about 27.8 GB in F8\_E4M3 and about 13.9 GB at 4-bit, as an estimate
- Versions: Hugging Face lists 9 quantized and 8 fine-tuned versions made by other people
- Hosted option: the card says an official API from Qwen Cloud is coming, with more production features such as 1M context by default

Context length in tokens

| Label | Value |
| --- | --- |
| Native | 262,144 tokens |
| Extended | 1,000,000 tokens |

- License: apache-2.0
- Cost to run: none named for the local files, no API key or account. The hosted Qwen Cloud service is separate
- Data it reaches: it downloads model files from Hugging Face, and the card text names no other service for the local run

## README

````markdown
# Qwen3.8-27B-FP8

Handles text, images and video with FP8-quantized weights of Qwen3.8-27B, a 27B dense model with adjustable thinking. For developers serving it with transformers, vLLM or SGLang.

Made by Qwen 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 FP8-quantized weights of Qwen3.8-27B in Hugging Face Transformers format. Qwen3.8-27B is a dense vision-language model that reads images and video, and it is built for coding, research and long multi-step agent tasks. The card says the FP8 version performs nearly the same as the original.

Who it suits: It suits developers who want a compact dense model for coding and agent work with less memory than the full-precision files need, and teams who serve models with vLLM or SGLang.

```table
Industry | Who uses it | How they use it | Example
Software | Developers | Run a 27B model for coding and agent tasks | Ask for help on a multi-step coding job
Research | Analysts | Use thinking control for deeper reasoning on hard questions | Raise reasoning effort for a research question
Hosting | Teams serving models | Load the FP8 files in vLLM or SGLang | Serve a vision-language model to internal tools
```

People who want a managed service without running anything can skip the weights, since the card mentions an official hosted option from Qwen Cloud.

Where to use it:

- Hugging Face Transformers, in the format the repository holds
- vLLM, SGLang and TokenSpeed, which the card says the files work with
- Windows, macOS or Linux, where you can download the files with Python

```table
Use case | Who | What they get back
Agentic coding | Developers | Planning and better handling of environment feedback across long tasks
Image and video understanding | Analysts | Answers about diagrams, documents and long videos
Reasoning control | Application builders | Thinking that is on by default and can be turned off per request
```

How to set it up: You need these first:

- Python and the huggingface_hub package
- Room for the files, about 27.8 GB as F8_E4M3 or about 13.9 GB at 4-bit as an estimate
- One of the engines the card names, such as Transformers, vLLM or SGLang

1. Install the Hugging Face hub package.
2. Download the model files to your computer.
3. Load them in Transformers, vLLM, SGLang or TokenSpeed.

```sh
pip install -U huggingface_hub
hf download Qwen/Qwen3.8-27B-FP8
```

How to use it: Download the files, load them in an engine the card names, then chat with the model, with images or video if you like. The card text here has no run commands, so follow its model card for the exact calls.

```table
Tool | What it does
Thinking mode | On by default and can be disabled for each request
reasoning_effort | Tunes how deeply the model reasons
preserve_thinking | Keeps reasoning context from earlier messages
```

Example: A developer could ask the model to plan and carry out a multi-step coding task, with reasoning_effort set higher for a hard part. The card says the model is tuned to carry such tasks through to completion.

For newcomers and experts: This is a mid-sized model that reads text, pictures and video. It is quantized to FP8, which makes the files smaller. A first small step is to download them and read the card's notes on thinking control.

The card says the quantization is fine-grained FP8 with a block size of 128. The model has 64 layers laid out as sixteen repeats of three Gated DeltaNet blocks and one Gated Attention block, each followed by a feed-forward network. The hidden dimension is 5120, and multi-token prediction was trained with several steps. Context is 262,144 tokens natively and can reach 1,000,000.

```flow
Download the FP8 weights with hf
Load them in a supported engine
Send text, images or video
Set thinking and effort as needed
Read the reply
```

Good to know:

- Size: 27.8B parameters per Hugging Face, about 27.8 GB in F8_E4M3 and about 13.9 GB at 4-bit, as an estimate
- Versions: Hugging Face lists 9 quantized and 8 fine-tuned versions made by other people
- Hosted option: the card says an official API from Qwen Cloud is coming, with more production features such as 1M context by default

```chart
Title: Context length in tokens
Native: 262,144 tokens
Extended: 1,000,000 tokens
```

- License: apache-2.0
- Cost to run: none named for the local files, no API key or account. The hosted Qwen Cloud service is separate
- Data it reaches: it downloads model files from Hugging Face, and the card text names no other service for the local run

## On Hugging Face, 13 November 2026

- Task: image text to text
- Library: transformers
- Parameters: 27.8B
- Weights: about 27.8 GB in F8_E4M3; about 13.9 GB at 4-bit (estimate)
- Formats: Safetensors
- Downloads in the last 30 days: 5,442,810
- Likes: 984
- License: apache-2.0
- Based on: Qwen/Qwen3.8-27B
- Quantized versions: 9
- Fine-tunes: 8
- Tags: transformers, safetensors, qwen3_5, image-text-to-text, conversational, fp8

## Links

- Model: https://huggingface.co/Qwen/Qwen3.8-27B-FP8
- Files: https://huggingface.co/Qwen/Qwen3.8-27B-FP8/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download Qwen/Qwen3.8-27B-FP8
```

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

[Qwen/Qwen3.8-27B-FP8 on Hugging Face](https://huggingface.co/Qwen/Qwen3.8-27B-FP8)

## 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-FP8 (this page) | 5,442,810 | 984 | 27.8B | apache-2.0 | 2026-11-07 |
| [KVARK Sparkle 1.0 (27B) FP8](https://mindket.com/p/acc7b74d-4609-4c56-abe3-47d4ccc946e3) | 24,270 | 5 | 27.8B | apache-2.0 | 2026-10-29 |
| [GLM-5.3-Flash](https://mindket.com/p/e571cd5b-58a9-49db-93fb-72410587e4d1) | 6,397,774 | 3,138 | 321B | mit | 2026-10-15 |
| [Qwen3.6-35B-A3B-FP8](https://mindket.com/p/ba2b931a-8b9a-43c9-9290-bc06e5a36c0a) | 3,818,590 | 448 | 36B | apache-2.0 | 2026-11-07 |
| [DeepSeek-V4.1-Flash](https://mindket.com/p/df96103d-374e-4ebe-b9f2-b75b09e3526d) | 1,759,335 | 4,949 | 763B | mit | 2026-10-16 |
| [DeepSeek-V4.1-Flash-1017](https://mindket.com/p/dc968c8e-249a-450a-af4c-bd0b11b76d78) | 398,161 | 5,600 | 763B | mit | 2026-11-10 |

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 · erdaltoprak · 2026-08-14](https://news.ycombinator.com/item?id=49299605): Qwen 3.8 27B
- [Bluesky · Willem Meints · 2026-08-19](https://bsky.app/profile/willemmeints.bsky.social/post/3mtg27iqzqk2p): I have some updates to make to my talk about running open-source models for coding tasks. Qwen 3.8 is here\! And I have a different agent setup, yet again ;-) huggingface.co/Qwen/Qwen3.8...
- [Hacker News · xlayn · 2026-08-17](https://news.ycombinator.com/item?id=49325979): Qwen 3.8 27B is excellent, but it defaults to overthinking things: I have this branch of llama.cpp that among other things (like patching the template to not break the kv cache, and saving conversations to disk so you can resume quickly days after) also accept the reasoning effort flag here https://github.com/alainnothere/llama.cpp/tree/disk-cache-ev... I did…
- [Bluesky · Нейролента · 2026-09-12](https://bsky.app/profile/nairolenta.bsky.social/post/3mvdir6vmg42p): 🛠 Локальные LLM на DGX Spark: 256K контекста ценой параллелизма Два дня на развёртывание двух Qwen-моделей через vLLM на NVIDIA DGX Spark показали, где заканчивается «модель запустилась» и начинается «сервис работает». Основная Qwen3.8-27B-FP8 и… Читать в Telegram: https://t.me/nairolenta/254
- [Hacker News · silentbob7 · 2026-08-16](https://news.ycombinator.com/item?id=49319604): Qwen 3.8 27B: Here's my model.ini for 4090 (using the chat template from froggeric): version = 1 \* ; Global defaults shared across all presets threads = 16 Qwen3.8-27B ; Model files model = ..\\Qwen3.8-27B-FP8\\Qwen3.8-27B-UD-Q4\_K\_XL.gguf mmproj = ..\\Qwen3.8-27B-FP8\\mmproj-BF16.gguf ; Context \& compute…
- [Bluesky · デイリーHuggingFaceトレンド · 2026-08-20](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mtizudb7ki2a): 今日のHuggingFaceトレンド Qwen/Qwen3.8-27B-FP8 本リポジトリは、Qwen3.8-27BモデルのFP8量子化済み重みと設定ファイルを提供することを目的としています。 このモデルは画像と動画を理解できる視覚言語モデルであり、コーディングや研究、エージェントタスクなどの複雑な処理に適しています。 Hugging Face TransformersやvLLM、SGLangなどで利用可能で、量子化後も元のモデルと同等の性能を維持しています。
- [Hacker News · nblgbg · 2026-08-14](https://news.ycombinator.com/item?id=49299847): Qwen 3.8 27B: Is there any advantage to using the model from Unsloth compared with https://huggingface.co/Qwen/Qwen3.8-27B-FP8 ?
- [Bluesky · Willem Meints · 2026-08-19](https://bsky.app/profile/willemmeints.bsky.social/post/3mth5obflas2f): I found out that if you install the docker model runner plugin you can run huggingface models much easier on a Spark DGX machine\! Run \`sudo apt-get apt-get install docker-model-plugin\` to get the plugin for Docker on your Spark DGX. Next, run \`docker model run hf.co/Qwen/Qwen3.8-27B-FP8\`.
- [Bluesky · デイリーHuggingFaceトレンド · 2026-08-18](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mtdyuqsvms2y): 今日のHuggingFaceトレンド Qwen/Qwen3.8-27B-FP8 Qwen3.8-27BのFP8量子化済みモデル重みと設定ファイルを提供することを目的としています。 本モデルは画像や動画を理解する視覚言語モデルで、コーディングや研究などの複雑なタスクに対応します。 FP8量子化によって元の性能を維持しつつ、vLLMやSGLang等の環境で効率的にデプロイすることが可能です。
- [Bluesky · Hacker News 750 Points · 2026-08-14](https://bsky.app/profile/newsyc750.bsky.social/post/3mt37oio62e2n): Qwen 3.8 27B https://huggingface.co/Qwen/Qwen3.8-27B-FP8 (http://news.ycombinator.com/item?id=49299605)
- [Bluesky · 角 征典／Masanori Kado · 2026-08-14](https://bsky.app/profile/kdmsnr.com/post/3mt2vokbp3z2c): 🌐クウェン 3.8 27B https://huggingface.co/Qwen/Qwen3.8-27B-FP8 via \#HackerNews
- [Bluesky · Hacker News 200 Points · 2026-08-14](https://bsky.app/profile/newsyc200.bsky.social/post/3mt2oe3lug52v): Qwen 3.8 27B https://huggingface.co/Qwen/Qwen3.8-27B-FP8 (http://news.ycombinator.com/item?id=49299605)

## 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: 2026-11-07
- First published: 2026-08-13
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open Qwen/Qwen3.8-27B-FP8 on Hugging Face](https://huggingface.co/Qwen/Qwen3.8-27B-FP8)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/79f1a984-4e81-485e-999d-dba1a831f37a)
- [Its page on Mindket](https://mindket.com/p/79f1a984-4e81-485e-999d-dba1a831f37a)