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How to run DeepSeek-V4.1-Flash locally with picture input with Project Maya

Set up DeepSeek-V4.1-Flash-1017 on your GPUs and include vision support for images.

Authorization notice. Project Maya is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Reverse engineering. The DeepSeek-V4.1-Flash-1017 GGUF files and the optional DSpark drafter are large third-party or repo-hosted model weights, so verify checksums and licenses before relying on them.

Who it is forAn engineer with a multi-GPU or large-memory machine who wants a second frontier-class model and needs image understanding.

The problemFew open models of this size run on personal hardware, and many do not handle pictures.

The outcomeA local DeepSeek-V4.1-Flash-1017 server that answers text and picture prompts, with its own numbers in the monitor.

What you need first

  • A PC with one or more NVIDIA or AMD GPUs, running Linux or Windows (AMD and Windows are marked experimental in the README)
  • Enough combined RAM, VRAM and NVMe SSD space for the model you pick (Maya-S is 96.5 GB; DeepSeek-V4.1-Flash-1017 GGUF files are 340 GB or more)
  • Time and bandwidth to download the model files from Hugging Face
  • A terminal to run ./setup.sh

Steps

  1. Run ./setup.sh --model DeepSeek-V4.1-Flash-1017 to set up the DeepSeek engine (maya-ds4).
  2. Leave vision on for the GGUF files, since the setup fetches DeepSeek's vision tower; use --no-vision only if you want to skip it.
  3. Run ./maya.sh --bench to measure speed on your GPUs.
  4. Send one request that includes an image and check the answer describes the picture. (inferred, check the README)

Prompt for your agent

Use the GitHub repo mw00/project-maya. On <target machine>, run ./setup.sh --model DeepSeek-V4.1-Flash-1017 and then ./maya.sh --bench. Expected output: benchmark numbers for the DeepSeek model and a successful image-plus-text answer. Verify that the vision component loaded (the setup should report it) before counting the test as passed.

Caveat. The README says the GGUF files total 340 GB or more, so this setup needs a large NVMe SSD and enough RAM plus VRAM before the first run.

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