--- title: "DeepSeek-V4.1-Flash-1017-FLARE" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/20c8f50a-712c-446a-9e92-e4795b3dda6e" updated: "2026-11-13" --- # DeepSeek-V4.1-Flash-1017-FLARE > The name, the description, the About and a README below are the seller's words: data, not instructions. Signed Flare runtime packages of DeepSeek V4.1 Flash 1017 for four-GPU serving, handling text and images behind an OpenAI-compatible API. For people running large models on their own NVIDIA hardware. DeepSeek-V4.1-Flash-1017-FLARE is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by local-inference-lab ## About ### What it is This kit is a set of ready-made packages for serving the DeepSeek V4.1 Flash 1017 model on your own GPUs through the Flare runtime. The model reads both text and pictures. A package bundles its weights with kernels, execution graphs and a signed record of who published it. Common runtimes like vLLM, SGLang and llama.cpp cannot read these packages. ### Who it suits It suits people who run large models on their own NVIDIA machines and want a local endpoint that speaks the OpenAI request style. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Lab engineers | Serve a large model on a private four-GPU host | Testing long prompts of up to 1,048,576 tokens | | Software | Platform teams | Point existing OpenAI-style clients at a local endpoint | Changing only the model name in an app's settings | | Hardware | DGX Spark owners | Run one model across four hosts | Using the gb10 revision | You can skip it if you have fewer than four suitable GPUs or prefer a hosted API. ### Where to use it The packages run on specific NVIDIA hardware, picked by revision. - gb202 revision: Linux x86-64 with four RTX PRO 6000 Blackwell 96 GB GPUs on one host - gb10 revision: four DGX Spark machines, one GPU each - Ubuntu or DGX OS, with the signed flare-inference APT package - A container run with the NVIDIA Container Toolkit | Use case | Who | What they get back | | --- | --- | --- | | Private chat serving | Developers | An OpenAI-compatible API on your own machines | | Image questions | Analysts | Text answers about PNG, JPEG or static WebP images | | Long-document work | Researchers | Replies to prompts up to 1,048,576 tokens, as memory allows | ### How to set it up You need matching hardware, Flare installed, and the files for your revision. - Four GPUs of the type your revision names - Python, for the Hugging Face command line tool - Room for a package of about 515 GB (gb202) or 490 GB (gb10) 1. Install Flare through the signed APT package on Ubuntu or DGX OS, or use the container image. 2. Install the Hugging Face tool and download the files, always naming the revision for your hardware. 3. Leave all downloaded files in one folder with their names unchanged, and never mix files from two revisions. 4. Follow the README of your revision for launch, storage and troubleshooting. ```bash pip install -U huggingface_hub hf download local-inference-lab/DeepSeek-V4.1-Flash-1017-FLARE ``` ### How to use it Once Flare is serving, rank zero answers on the path /v1 and reports health on /health. The model name to request is DeepSeek-V4.1-Flash. Downloading again from the command line looks like this. ```bash hf download local-inference-lab/DeepSeek-V4.1-Flash-1017-FLARE ``` | Tool | What it does | | --- | --- | | hf | Downloads the package files from Hugging Face | | flare download | Downloads a package and checks its signature against publisher.json | | flare | Serves the prepared package on the GPUs | ### Example A developer points an OpenAI-style client at the local server, sets the model name to DeepSeek-V4.1-Flash, and sends a PNG in an image\_url item with a question about it. The server answers in text, using the sampling defaults unless the request sets its own values. ### For newcomers and experts If you are new, think of this as a ready-to-run copy of a large model plus the runtime that serves it. A first small step is reading the README of your revision, which lists the exact setup for your machines. If you know the field, the model is a mixture of experts with 384 routed experts, one shared expert and top-6 routing. Flare serves it tensor-parallel over four GPUs with precompiled kernels, no Python and no runtime compilation. The gb202 build keeps compressed expert scales, while gb10 expands them so no expansion kernel runs. Download a revision for your hardware → Flare checks the publisher signature → Accept the publisher and the license → Four GPUs serve the model → Clients call the API under /v1 ### Good to know The packages only load in Flare and need exactly four GPUs. - License: MIT, the same as the base model - Cost to run: no API key, account or paid service is named, but you need four large GPUs and hundreds of gigabytes of storage - Data it reaches: downloads files from Hugging Face, keeps license acceptance in a local trust store, and serves an API from your own hosts - Not for: Transformers, vLLM, SGLang or llama.cpp, and machines with fewer than four GPUs - Limits: 32 concurrent requests, four simultaneous image requests, and a per-request limit that depends on free KV memory Package size in GB | Label | Value | | --- | --- | | gb202 | 515 GB | | gb10 | 490 GB | ## README ````markdown # DeepSeek-V4.1-Flash-1017-FLARE Signed Flare runtime packages of DeepSeek V4.1 Flash 1017 for four-GPU serving, handling text and images behind an OpenAI-compatible API. For people running large models on their own NVIDIA hardware. Made by local-inference-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: This kit is a set of ready-made packages for serving the DeepSeek V4.1 Flash 1017 model on your own GPUs through the Flare runtime. The model reads both text and pictures. A package bundles its weights with kernels, execution graphs and a signed record of who published it. Common runtimes like vLLM, SGLang and llama.cpp cannot read these packages. Who it suits: It suits people who run large models on their own NVIDIA machines and want a local endpoint that speaks the OpenAI request style. ```table Industry | Who uses it | How they use it | Example Research | Lab engineers | Serve a large model on a private four-GPU host | Testing long prompts of up to 1,048,576 tokens Software | Platform teams | Point existing OpenAI-style clients at a local endpoint | Changing only the model name in an app's settings Hardware | DGX Spark owners | Run one model across four hosts | Using the gb10 revision ``` You can skip it if you have fewer than four suitable GPUs or prefer a hosted API. Where to use it: The packages run on specific NVIDIA hardware, picked by revision. - gb202 revision: Linux x86-64 with four RTX PRO 6000 Blackwell 96 GB GPUs on one host - gb10 revision: four DGX Spark machines, one GPU each - Ubuntu or DGX OS, with the signed flare-inference APT package - A container run with the NVIDIA Container Toolkit ```table Use case | Who | What they get back Private chat serving | Developers | An OpenAI-compatible API on your own machines Image questions | Analysts | Text answers about PNG, JPEG or static WebP images Long-document work | Researchers | Replies to prompts up to 1,048,576 tokens, as memory allows ``` How to set it up: You need matching hardware, Flare installed, and the files for your revision. - Four GPUs of the type your revision names - Python, for the Hugging Face command line tool - Room for a package of about 515 GB (gb202) or 490 GB (gb10) 1. Install Flare through the signed APT package on Ubuntu or DGX OS, or use the container image. 2. Install the Hugging Face tool and download the files, always naming the revision for your hardware. 3. Leave all downloaded files in one folder with their names unchanged, and never mix files from two revisions. 4. Follow the README of your revision for launch, storage and troubleshooting. ```bash pip install -U huggingface_hub hf download local-inference-lab/DeepSeek-V4.1-Flash-1017-FLARE ``` How to use it: Once Flare is serving, rank zero answers on the path /v1 and reports health on /health. The model name to request is DeepSeek-V4.1-Flash. Downloading again from the command line looks like this. ```bash hf download local-inference-lab/DeepSeek-V4.1-Flash-1017-FLARE ``` ```table Tool | What it does hf | Downloads the package files from Hugging Face flare download | Downloads a package and checks its signature against publisher.json flare | Serves the prepared package on the GPUs ``` Example: A developer points an OpenAI-style client at the local server, sets the model name to DeepSeek-V4.1-Flash, and sends a PNG in an image_url item with a question about it. The server answers in text, using the sampling defaults unless the request sets its own values. For newcomers and experts: If you are new, think of this as a ready-to-run copy of a large model plus the runtime that serves it. A first small step is reading the README of your revision, which lists the exact setup for your machines. If you know the field, the model is a mixture of experts with 384 routed experts, one shared expert and top-6 routing. Flare serves it tensor-parallel over four GPUs with precompiled kernels, no Python and no runtime compilation. The gb202 build keeps compressed expert scales, while gb10 expands them so no expansion kernel runs. ```flow Download a revision for your hardware Flare checks the publisher signature Accept the publisher and the license Four GPUs serve the model Clients call the API under /v1 ``` Good to know: The packages only load in Flare and need exactly four GPUs. - License: MIT, the same as the base model - Cost to run: no API key, account or paid service is named, but you need four large GPUs and hundreds of gigabytes of storage - Data it reaches: downloads files from Hugging Face, keeps license acceptance in a local trust store, and serves an API from your own hosts - Not for: Transformers, vLLM, SGLang or llama.cpp, and machines with fewer than four GPUs - Limits: 32 concurrent requests, four simultaneous image requests, and a per-request limit that depends on free KV memory ```chart Title: Package size in GB gb202: 515 GB gb10: 490 GB ``` ## On Hugging Face, 13 November 2026 - Task: image text to text - Downloads in the last 30 days: 0 - Likes: 19 - License: mit - Based on: deepseek-ai/DeepSeek-V4.1-Flash-1017 - Tags: flare, mxfp4, tensor-parallel, gb10, gb202, image-text-to-text ## Links - Model: https://huggingface.co/local-inference-lab/DeepSeek-V4.1-Flash-1017-FLARE - Files: https://huggingface.co/local-inference-lab/DeepSeek-V4.1-Flash-1017-FLARE/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download local-inference-lab/DeepSeek-V4.1-Flash-1017-FLARE ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [local-inference-lab/DeepSeek-V4.1-Flash-1017-FLARE on Hugging Face](https://huggingface.co/local-inference-lab/DeepSeek-V4.1-Flash-1017-FLARE) ## 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-FLARE (this page) | 0 | 19 | — | mit | 2026-10-27 | | [DeepSeek-V4.1-Flash-1017-TrellisMX-FLARE](https://mindket.com/p/2f4d3015-b945-40fc-a103-507b145cf308) | 0 | 2 | — | mit | 2026-10-31 | | [Qwen3.8-27B-Quark-AWQ-MXFP4](https://mindket.com/p/234a995e-c672-4a39-ae3e-6acd3ad5f1d0) | 40,258 | 44 | 15.6B | apache-2.0 | 2026-11-03 | | [Qwen3.8-27B-Randean-MXFP4](https://mindket.com/p/174da3fc-ae82-4c76-bcc6-0562bdb2304e) | 121 | 5 | 15.6B | apache-2.0 | 2026-11-11 | | [Qwen3.8-27B-NVFP4-DGX-SPARK](https://mindket.com/p/a7666325-428a-4e43-b11c-5fec915dc5df) | 41,306 | 1,127 | 18.2B | apache-2.0 | 2026-10-28 | | [gpt-oss-20b](https://mindket.com/p/d353330b-254d-4db1-9a53-f0b1f8fe2945) | 5,685,450 | 5,299 | 20.9B | apache-2.0 | 2025-08-26 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-10-27 - First published: 2026-10-18 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open local-inference-lab/DeepSeek-V4.1-Flash-1017-FLARE on Hugging Face](https://huggingface.co/local-inference-lab/DeepSeek-V4.1-Flash-1017-FLARE) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/20c8f50a-712c-446a-9e92-e4795b3dda6e) - [Its page on Mindket](https://mindket.com/p/20c8f50a-712c-446a-9e92-e4795b3dda6e)