Recipe card only. This repo holds no weights. The weights are in two other repos (see Get the weights). The recipe is on GitHub: Elevatormusic/DeepSeek-V4.1-Flash-1017-EXL3-TensorFold-2x-DGX-Spark.

DeepSeek V4.1 Flash on two DGX Spark

Run DeepSeek's V4.1 Flash model on two NVIDIA DGX Spark computers, twice as fast as the standard setup, and ready for AI agents.

Measured on 17 October 2026 on one pair of DGX Spark. Every number on this page comes from a saved run in the GitHub recipe.

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At a glance

A token is a piece of a word. 100 tokens are about 75 English words.

Both setups ran on the same two computers, with the same model files and the same prompts, on the same day.

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What you get

• Twice the speed. Replies come about twice as fast as with the standard vLLM setup.
• Ready for AI agents. The model can call tools when you tell it to. It can also reply in an exact format, such as JSON that matches your schema.
• Four conversations at once. Up to 4 requests share the two computers. Together they reach 140 tokens/s on code.
• It remembers long documents. Ask a second question about the same long document. The reply starts in about 1 second instead of 13. After a restart, it starts in about 3 seconds.
• It keeps running. One bad request ends only that request. It does not stop the whole server.
• A very long memory. The model can read up to 1 million tokens in one conversation.

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Get the weights

This recipe uses two sets of files. Both are already public. The recipe downloads them for you at these exact versions.

The Engram tables of the 1017 release are new. Do not use the tables of the older V4.1 Flash release.

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What you need

• Two NVIDIA DGX Spark, joined by one QSFP cable between their ConnectX-7 network ports.
• About 500 GB of free disk on each computer. The model files use 385 GB. The first start uses 110 GB more.
• About 100 GB of free memory on each computer. Close other AI programs first.
• A network setup with fixed addresses on the cable link and ssh from the first computer to the second. The GitHub recipe has a network guide.
• Time. Most of the first setup is the 400 GB download. It takes a few hours on a fast internet link.

The head is the computer where you type the commands. The worker is the second computer.

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Quick start

Run each command on the head.

git clone https://github.com/Elevatormusic/DeepSeek-V4.1-Flash-1017-EXL3-TensorFold-2x-DGX-Spark
cd DeepSeek-V4.1-Flash-1017-EXL3-TensorFold-2x-DGX-Spark

1. Make your settings file. Then open .env and type your addresses and folder paths.

   cp .env.example .env
   

2. Download the model. This step gets 400 GB, checks every file, and copies it to the worker.

   scripts/download.sh
   

3. Prepare the chat template. This step gets the template and applies a small fix.

   scripts/patch_template.py --worker
   

4. Build the engine. This step builds the server software and copies it to the worker.

   docker/build.sh
   

5. Start the server. It checks your setup first. The first start takes about 15 minutes. Later starts take 2 to 4 minutes.

   ./start.sh
   

6. Check that it works. This sends five real requests and shows the result of each.

   ./verify.sh
   

To stop the server, run ./stop.sh. To see if it runs, run ./status.sh.

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Connect your app

The server speaks the OpenAI API. Any app that supports this API can use it.

Thinking is off by default. To turn it on, do one of these in your request:

• Set reasoning_effort to one of none, minimal, low, medium, high, xhigh or max.
• Set chat_template_kwargs.enable_thinking to true.

curl http://127.0.0.1:8888/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "DeepSeek-V4.1-Flash-1017",
    "messages": [{"role": "user", "content": "Why is the sky blue?"}],
    "reasoning_effort": "medium"
  }'

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Good to know

• The first long question after a start is slower. It takes about 6 seconds more. Later questions do not.
• Long documents load slower than bertholomus reports. With 32,000 tokens or more, this setup reads text 30 to 37% slower than their published numbers. The cause is not known yet.
• The standard setup was tested one request at a time only. So the "four at once" numbers have no vLLM comparison.
• Living Weights is not available. That TensorFold feature works only with a different model on one computer.

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Credits

This recipe stands on the work of other people. Thank you.

• bertholomus (Bertholomus AI) wrote the fast V4.1 engine that this recipe runs. It gives the DeepSeek V4.1 model code, the kernels, the DSpark drafting, the Engram support and the fast link between the two computers. All of the speed comes from their work. See their recipe: bertholomus/DeepSeek-V4.1-Flash-TensorFold-TP2-2xGB10.
• LibertAIDAI made the compressed EXL3 2.9 bpw weights.
• DeepSeek made the model, its Engram tables and the reference prompt format.
• turboderp made ExLlamaV3, the EXL3 format.
• ashhart and the TensorFold contributors made TensorFold, the engine framework.
• willjriley and Capicua25x made fixes to the engine.
• MiaAI-Lab made the vLLM kit that gives the standard-setup numbers, and the chat template.
• tonyd2wild made the benchmark method.

What this recipe adds to the engine is four changes: forced tool calls, prompts that match DeepSeek's reference byte for byte, replies in an exact format, and fault isolation. The engine changes are in the fork Elevatormusic/TensorFold, in pull requests #1, #2 and #3.

This recipe is not affiliated with or endorsed by DeepSeek, NVIDIA, LibertAIDAI, the TensorFold authors, bertholomus or MiaAI-Lab.

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Licence

The scripts, tests and documents in the recipe are Apache-2.0. The model, the chat template and the engine keep their own licences: