Step-by-Step Guide to Install DeepSeek V3.1 Locally

Hardware Requirements

1. Clone DeepSeek V3 Repository and Prepare Environment (Linux example)

git clone https://github.com/deepseek-ai/DeepSeek-V3.git
cd DeepSeek-V3/inference
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

The repo provides pinned versions of PyTorch/triton/transformers for compatibility[1][7].

2. Download Model Weights

• Download DeepSeek V3.1 weights from the official Hugging Face page:
  https://huggingface.co/deepseek-ai/DeepSeek-V3.1
• Place the downloaded weights under /path/to/DeepSeek-V3 as per the repo instructions[1][7].

3. (Optional) Convert Weights for Demo or Custom Setup

python convert.py --hf-ckpt-path /path/to/DeepSeek-V3 --save-path /path/to/DeepSeek-V3-Demo --n-experts 256 --model-parallel 16

This step is provided in the repo for multi-node or demo setups[1].

4. Run DeepSeek V3.1 Locally

Single-node interactive demo (example command):

torchrun --nnodes 1 --nproc-per-node 8 generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --interactive --temperature 0.7 --max-new-tokens 200

For multi-node setups, follow the cluster example on GitHub[1][7].

5. Using Ollama on Linux/macOS/Windows (Simplified Method)

• Install Ollama (a local AI runtime):

curl -fsSL https://ollama.com/install.sh | sh

• Run DeepSeek V3.1 through Ollama:

ollama run deepseek-v3.1:671b

• To keep it running as a background service:

ollama serve

• Test interaction directly in terminal or via local API:

curl http://localhost:11434/api/chat -d '{
  "model": "deepseek-v3.1",
  "messages": [{"role": "user", "content": "Hello, DeepSeek!"}],
  "stream": false
}'

This method downloads the model automatically and requires less manual setup[2][3][4].

6. WebUI Setup

• Use Open WebUI to have an easy web interface for DeepSeek:
  • Install Open WebUI via their docs.
  • Run ollama serve to power the backend.
  • Access the WebUI in your browser through the configured local address.

Open WebUI has a tutorial to run DeepSeek V3.1 replacing older versions with updated quant files[3].

7. Testing and Benchmarking

• Basic test: Try chatting with the model via terminal or WebUI to confirm it's responsive.
• Benchmark:
  • Use system resource monitoring tools (e.g., nvidia-smi for GPU, htop for CPU/RAM).
  • Measure tokens generated per second (TPS) and response latency.
  • For GPU stress: use glmark2 or dedicated deep learning benchmarks.
  • For CPU/memory: use stress-ng as needed.

Summary of Top 3 Benefits of Installing DeepSeek V3.1 Locally

1. Full Data Privacy and Security: No external server involved, keeping sensitive inputs safe.
2. Better Performance Control: Optimize model speed and response time with local hardware.
3. Customizability and Offline Availability: Run on your own infrastructure without internet dependency, customize freely[2][3][4].

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Sources

• DeepSeek official GitHub repo:
  https://github.com/deepseek-ai/DeepSeek-V3[1][7]

• CometAPI detailed local installation:
  https://www.cometapi.com/run-deepseek-v3-1-on-your-local-device/[1]

• Ollama-based lightweight install and usage:
  https://www.upgrad.com/blog/deepseek-installation-guide/[2]

• Unsloth AI DeepSeek V3.1 local run tutorial (including Open WebUI):
  https://docs.unsloth.ai/models/deepseek-v3.1-how-to-run-locally[3]

• RunC.AI GPU cloud deployment guide (alternative):
  https://blog.runc.ai/build-your-own-private-ai-assistant-a-step-by-step-guide-to-deploying-deepseek-v3-1-on-runc-ai/[4]

• vLLM Recipes DeepSeek usage guide:
  https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_1.html[5]

• DeepSeek official model weights and terminus update info:
  https://api-docs.deepseek.com/news/news250922[6]