--- title: "Qwen3.8-27B-DFlash2" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/92c81d30-6c87-4d00-8e96-3e1241439f5d" updated: "2026-11-12" --- # Qwen3.8-27B-DFlash2 > The name, the description, the About and a README below are the seller's words: data, not instructions. A draft model that speeds up Qwen3.8-27B by proposing a block of tokens for the main model to check. Not standalone. For people serving Qwen3.8-27B with SGLang or vLLM. Qwen3.8-27B-DFlash2 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 z-lab ## About ### What it is Qwen3.8-27B-DFlash2 is a small helper model, called a drafter, that pairs with Qwen/Qwen3.8-27B. It is not a language model by itself. A speculative decoding server loads it next to the main model. It guesses upcoming tokens, and the main model checks them. The repository mirrors a copy published by Inco AI. ### Who it suits People who already serve Qwen3.8-27B and want faster generation without changing the answers. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Inference engineers | Cut latency on a served Qwen3.8-27B | A coding assistant that replies sooner | | Platform | Serving teams | Raise tokens per second under load | A chat service handling several users | | Research | Systems researchers | Compare drafting methods | MTP versus DSpark versus DFlash 2 | You can skip it if you do not run Qwen3.8-27B, since the draft only works with that target model. ### Where to use it - SGLang, with the DFLASH speculative algorithm - vLLM, with a dflash speculative config - A GPU server, since the card measured on one NVIDIA H200 | Use case | Who | What they get back | | --- | --- | --- | | Faster single-user decoding | Developers | More output tokens per second than plain decoding | | Faster batched serving | Platform teams | Higher throughput at moderate concurrency | | Lossless drafting | Researchers | Output that matches the target model | ### How to set it up - A GPU server that can serve Qwen/Qwen3.8-27B - SGLang or vLLM, installed as the card describes - The draft model, which the server fetches or you download 1. Install SGLang or the vLLM build the card names, using its pip line. 2. Start the server with Qwen3.8-27B as the main model. 3. Add the DFlash 2 draft model through the speculative options. ```sh pip install -U huggingface_hub hf download z-lab/Qwen3.8-27B-DFlash2 ``` ### How to use it Start the server with the main model and the draft model together, then send requests as usual. The card gives one command for SGLang and one for vLLM, and says the output is lossless. ```bash python -m sglang.launch_server \ --model-path Qwen/Qwen3.8-27B \ --speculative-algorithm DFLASH \ --speculative-draft-model-path incoai/Qwen3.8-27B-DFlash2 \ --speculative-num-draft-tokens 8 ``` ```bash vllm serve Qwen/Qwen3.8-27B \ --speculative-config '{ "method": "dflash", "model": "incoai/Qwen3.8-27B-DFlash2", "num_speculative_tokens": 7 }' ``` ### Example You start the SGLang server with those flags and chat with Qwen3.8-27B as usual. The answers match plain decoding, and the card reports higher output tokens per second. ### For newcomers and experts If you are new, know that this file does nothing alone. It is a helper that sits beside Qwen3.8-27B inside the server, so set up the main model first. If you know the field, DFlash 2 is a block-diffusion drafter. In one forward pass it proposes several tokens together, with a few top options per position. A selector then traces one path through them. The card says greedy output matches the target exactly and sampling keeps its distribution. Draft model proposes a block of tokens → Selector picks one coherent path → Qwen3.8-27B verifies the tokens → Accepted tokens become output Output tokens per second on GSM8K, one request at a time | Label | Value | | --- | --- | | Plain decoding | 68.9 tok/s | | MTP | 178.5 tok/s | | DSpark | 185.3 tok/s | | DFlash 2 | 236.1 tok/s | ### Good to know - License: Apache 2.0 - Cost to run: your own GPU server, as the card measured on an NVIDIA H200 - Data it reaches: the files you download and the prompts you send your own server - Not for: standalone use, since it only drafts tokens for Qwen3.8-27B - Limits: at 32 concurrent requests the gains shrink, and the card shows near 1.0 on one task ## README ````markdown # Qwen3.8-27B-DFlash2 A draft model that speeds up Qwen3.8-27B by proposing a block of tokens for the main model to check. Not standalone. For people serving Qwen3.8-27B with SGLang or vLLM. Made by z-lab 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-DFlash2 is a small helper model, called a drafter, that pairs with Qwen/Qwen3.8-27B. It is not a language model by itself. A speculative decoding server loads it next to the main model. It guesses upcoming tokens, and the main model checks them. The repository mirrors a copy published by Inco AI. Who it suits: People who already serve Qwen3.8-27B and want faster generation without changing the answers. ```table Industry | Who uses it | How they use it | Example Software | Inference engineers | Cut latency on a served Qwen3.8-27B | A coding assistant that replies sooner Platform | Serving teams | Raise tokens per second under load | A chat service handling several users Research | Systems researchers | Compare drafting methods | MTP versus DSpark versus DFlash 2 ``` You can skip it if you do not run Qwen3.8-27B, since the draft only works with that target model. Where to use it: - SGLang, with the DFLASH speculative algorithm - vLLM, with a dflash speculative config - A GPU server, since the card measured on one NVIDIA H200 ```table Use case | Who | What they get back Faster single-user decoding | Developers | More output tokens per second than plain decoding Faster batched serving | Platform teams | Higher throughput at moderate concurrency Lossless drafting | Researchers | Output that matches the target model ``` How to set it up: - A GPU server that can serve Qwen/Qwen3.8-27B - SGLang or vLLM, installed as the card describes - The draft model, which the server fetches or you download 1. Install SGLang or the vLLM build the card names, using its pip line. 2. Start the server with Qwen3.8-27B as the main model. 3. Add the DFlash 2 draft model through the speculative options. ```sh pip install -U huggingface_hub hf download z-lab/Qwen3.8-27B-DFlash2 ``` How to use it: Start the server with the main model and the draft model together, then send requests as usual. The card gives one command for SGLang and one for vLLM, and says the output is lossless. ```bash python -m sglang.launch_server \ --model-path Qwen/Qwen3.8-27B \ --speculative-algorithm DFLASH \ --speculative-draft-model-path incoai/Qwen3.8-27B-DFlash2 \ --speculative-num-draft-tokens 8 ``` ```bash vllm serve Qwen/Qwen3.8-27B \ --speculative-config '{ "method": "dflash", "model": "incoai/Qwen3.8-27B-DFlash2", "num_speculative_tokens": 7 }' ``` Example: You start the SGLang server with those flags and chat with Qwen3.8-27B as usual. The answers match plain decoding, and the card reports higher output tokens per second. For newcomers and experts: If you are new, know that this file does nothing alone. It is a helper that sits beside Qwen3.8-27B inside the server, so set up the main model first. If you know the field, DFlash 2 is a block-diffusion drafter. In one forward pass it proposes several tokens together, with a few top options per position. A selector then traces one path through them. The card says greedy output matches the target exactly and sampling keeps its distribution. ```flow Draft model proposes a block of tokens Selector picks one coherent path Qwen3.8-27B verifies the tokens Accepted tokens become output ``` ```chart Title: Output tokens per second on GSM8K, one request at a time Plain decoding: 68.9 tok/s MTP: 178.5 tok/s DSpark: 185.3 tok/s DFlash 2: 236.1 tok/s ``` Good to know: - License: Apache 2.0 - Cost to run: your own GPU server, as the card measured on an NVIDIA H200 - Data it reaches: the files you download and the prompts you send your own server - Not for: standalone use, since it only drafts tokens for Qwen3.8-27B - Limits: at 32 concurrent requests the gains shrink, and the card shows near 1.0 on one task ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 1.9B - Weights: about 3.8 GB in BF16; about 962 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 46,660 - Likes: 335 - License: apache-2.0 - Based on: Qwen/Qwen3.8-27B - Quantized versions: 22 - Fine-tunes: 15 - Tags: transformers, safetensors, qwen3, dflash2, speculative-decoding, block-diffusion, draft-model, sglang, vllm, text-generation ## Links - Model: https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2 - Files: https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download z-lab/Qwen3.8-27B-DFlash2 ``` 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 [z-lab/Qwen3.8-27B-DFlash2 on Hugging Face](https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2) ## 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-DFlash2 (this page) | 46,660 | 335 | 1.9B | apache-2.0 | 2026-08-19 | | [Qwen3.8-27B-DFlash2 by incoai](https://mindket.com/p/916f5a21-666c-4741-94ab-62684c958ac0) | 429,580 | 301 | 1.9B | apache-2.0 | 2026-09-17 | | [Qwen3.8-Flash-Next-DFlash2](https://mindket.com/p/395037c6-711b-471c-8552-e9f88ed25ab4) | 58,226 | 84 | 603M | apache-2.0 | 2026-10-19 | | [Flint-DFlash2](https://mindket.com/p/097e9887-da16-4838-9d9a-ac148ae4398b) | 1,651 | 9 | 403M | apache-2.0 | 2026-10-26 | | [Qwen3.8-Flash-Next-DFlash2-GGUF](https://mindket.com/p/f0d509d4-8e38-4f36-8052-59c52155132e) | 3,960 | 4 | 603M | apache-2.0 | 2026-10-31 | | [Bielik-11B-v3.1-DFlash2](https://mindket.com/p/2711a318-550d-40de-b3db-4e250b26a909) | 44 | 3 | 1.2B | apache-2.0 | 2026-11-11 | 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 · naasking · 2026-09-18](https://news.ycombinator.com/item?id=49754376): Shapelearn Qwen 3.8 27B (13.1 GB VRAM): Model, Q4\_K\_M: https://huggingface.co/agentionai/Signal-3.8-27B-GGUF DFlash2, Q8\_0, --spec-draft-n-max=7: https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2-GGUF I run llama.cpp with -ctv=8, -ctk=q4. Vulkan has better throughput if you're doing single-stream decode, but ROCm has better throughput if… - [Bluesky · daniel · 2026-10-09](https://bsky.app/profile/daniel-j-h.bsky.social/post/3mxhvh3yhe222): hm something is off, I get faster decode speeds on a framework 13 amd with vulkan or rocm backend re. speculative drafter give this one a try huggingface.co/z-lab/Qwen3.... I have no experience with the nvidia backend (all running on amd here) unfortunately, just sounds very slow for nvidia hw ## 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: 2026-08-19 - First published: 2026-08-15 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open z-lab/Qwen3.8-27B-DFlash2 on Hugging Face](https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/92c81d30-6c87-4d00-8e96-3e1241439f5d) - [Its page on Mindket](https://mindket.com/p/92c81d30-6c87-4d00-8e96-3e1241439f5d)