DeepSeek-V4.1-Flash-1017-FLARE

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.

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.

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.

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

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:

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.