Deepseek R1 Enterprise Local Deployment Complete Manual

I. Introduction

Deepseek R1 is a high-performance general-purpose large language model that supports complex reasoning, multimodal processing, and technical document generation. This manual provides a complete local deployment guide for technical teams, covering hardware configurations, domestic chip adaptations, quantization solutions, heterogeneous solutions, cloud alternatives and deployment methods for the complete 671B MoE model.

II. Core configuration requirements for local deployment

1. Table of model parameters and hardware correspondence

2. Analysis of computing power requirements

Additional Notes:

1. VRAM RequirementsThe VRAM requirements listed in the table are minimum requirements, and it is recommended that 20%-30% of additional video memory be reserved for actual deployments to handle peak demand during model loading and operation.
2. Multi-GPU Configuration: For large-scale models (e.g. 32B+), it is recommended to use multiple GPUs in parallel to improve computational efficiency and stability.
3. calculation accuracy: FP8 and BF16 are the current mainstream high-efficiency computational accuracies, which can guarantee the model performance while reducing the graphics memory usage.
4. Applicable Scenarios: Models with different parameter scales are suitable for tasks of different complexity, and users can choose the appropriate model version according to their actual needs.
5. Enterprise Deployment: For very large-scale models such as 671B, it is recommended that a professional-grade GPU cluster (such as the NVIDIA A100) be deployed to meet high-performance computing requirements.

III. Domestic Chip and Hardware Adaptation Program

1. Domestic eco-partnership dynamics

2. Recommended configuration for national hardware

IV. Cloud deployment alternatives

1. Recommended domestic cloud service providers

2. International access channels (requires magic or foreign enterprise Internet access)

• NVIDIA NIM: Enterprise GPU Cluster Deployment (link)
• Groq: ultra-low latency reasoning (link)

V. Ollama+Unsloth deployment

1. Quantification scheme and model selection

Download Address:

• HuggingFace Model Library
• Unsloth AI Official Description

2. Hardware configuration recommendations

3. Deployment steps (Linux example)

1. Installation of dependent tools:

# 安装 llama.cpp（用于合并分片文件）
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
brew install llama.cpp

2. Download and merge model slices:

llama-gguf-split --merge DeepSeek-R1-UD-IQ1_M-00001-of-00004.gguf DeepSeek-R1-UD-IQ1_S.gguf

3. Install Ollama:

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

4. Create the Modelfile:

FROM /path/to/DeepSeek-R1-UD-IQ1_M.gguf
PARAMETER num_gpu 28  # 每块 RTX 4090 加载 7 层（共 4 卡）
PARAMETER num_ctx 2048
PARAMETER temperature 0.6
TEMPLATE "<|end▁of▁thinking $|>{{{ .Prompt }}}<|end▁of▁thinking|>"

5. Run the model:

ollama create DeepSeek-R1-UD-IQ1_M -f DeepSeekQ1_Modelfile

4. Performance tuning and testing

• Low GPU utilization: Upgrade high-bandwidth memory (e.g. DDR5 5600+).
• Extended Swap Space::

sudo fallocate -l 100G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

Full blood 671B Deployment order

• VLLM::

vllm serve deepseek-ai/deepseek-r1-671b --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager

• SGLang::

python3 -m sglang.launch_server --model deepseek-ai/deepseek-r1-671b --trust-remote-code --tp 2

VI. Notes and Risks

1. Cost alerts:

• 70B Model: Requires 3 or more 80G RAM graphics cards (e.g. RTX A6000), not feasible for single card users.
• 671B Model: Requires 8xH100 clusters for supercomputing center deployments only.

2. Alternative programs:

• Individual users are recommended to use cloud-based APIs (e.g., Silicon Mobility), which are maintenance-free and compliant.

3. National hardware compatibility:

• A customized version of the framework is required (e.g., Rise CANN, Mu Xi MXMLLM).

VII. Appendix: Technical support and resources

• Huawei Rise: Rise Cloud Services
• Mu Xi GPU: Free API Experience
• Lee Seok Han Blog: Full deployment tutorial

VIII. Heterogeneous GPUStack solutions

GPUStack Open Source Project

https://github.com/gpustack/gpustack/

Model Resource Measurement Tool

• GGUF Parser(https://github.com/gpustack/gguf-parser-go) is used to manually calculate the video memory requirements.

GPUStack

DeepSeek Full Platform Private Deployment

concluding remarks

Deepseek R1 Localized deployment requires extremely high hardware investment and technical thresholds, so individual users should be cautious and enterprise users should fully assess the needs and costs. Through localized adaptation and cloud services, you can significantly reduce the risk and improve efficiency. Technology has no limits, rational planning can reduce costs and increase efficiency!

Global Enterprise Personal Channel Schedule

1. Secret Tower Search
2. 360 Nano AI Search
3. Silicon-based flow
4. Byte Jump Volcano Engine
5. Baidu cloud Chifan, a virtual virtualization system created by Baidu.com
6. NVIDIA NIM
7. Groq
8. Fireworks
9. Chutes
10. Github
11. POE
12. Cursor
13. Monica
14. lambda (Greek letter Λλ)
15. Cerebras
16. Perplexity
17. Alibaba Cloud 100 Refinement

For environments that require magic or foreign corporate Internet access

Chip Business Support Schedule

Table 1: Cloud Vendors Supporting DeepSeek-R1

Table 2: Enterprises Supporting DeepSeek-R1

Table 3: Summary of enterprises supporting DeepSeek-R1