By Ryan A. on Jan 6, 2025
DeepSeek V3 is a state-of-the-art Mixture-of-Experts (MoE) model boasting 671 billion parameters. It excels in tasks like reasoning, code generation, and multilingual support, making it one of the top-performing open-source AI solutions. This guide details the deployment process for DeepSeek V3, emphasizing optimal hardware configurations and tools like ollama
for easier setup.
Key features that make DeepSeek V3 stand out:
Deploying DeepSeek V3 locally provides complete control over its performance and maximizes hardware investments.
To deploy the full base model of DeepSeek V3 efficiently, use the following configurations:
For the full list of system requirements, including the distilled models, visit the system requirements guide.
For the simplest deployment, use ollama. Ensure ollama is installed on your system, then start the model with a single command:
ollama run deepseek-v3
This command launches an interactive session, enabling you to interact with the model without needing to configure complex setups.
Run smaller, distilled versions of the model that have more modest GPU requirements.
ollama run deepseek-r1:1.5b
ollama run deepseek-r1:8b
ollama run deepseek-r1:14b
ollama run deepseek-r1:32b
ollama run deepseek-r1:70b
Clone the official DeepSeek V3 repository:
git clone https://github.com/deepseek-ai/DeepSeek-V3.git
cd DeepSeek-V3
Navigate to the inference
folder and install required dependencies:
cd inference
# Optional: Isolate dependencies
virtualenv env
source env/bin/activate
pip install -r requirements.txt
Download the weights from Hugging Face:
mkdir -p /path/to/DeepSeek-V3-Demo
# Save model weights to the directory above.
For advanced inference scenarios, use distributed PyTorch commands:
torchrun --nnodes 2 --nproc-per-node 8 --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --interactive --temperature 0.7 --max-new-tokens 200
torchrun --nnodes 2 --nproc-per-node 8 --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --input-file $FILE
A specialized framework for MoE models like DeepSeek V3, offering:
A versatile inference framework supporting FP8 and BF16 precision, ideal for scaling DeepSeek V3.
Optimize your deployment with TensorRT-LLM, featuring quantization and precision tuning (BF16 and INT4/INT8).
nvidia-smi
for real-time utilization tracking.Deploying DeepSeek V3 is now more streamlined than ever, thanks to tools like ollama
and frameworks such as TensorRT-LLM and SGLang. By leveraging high-end GPUs like the NVIDIA H100 and following this guide, you can unlock the full potential of this powerful MoE model for your AI workloads.
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