---
title: "DeepSeek-R1 671B Deployment: How to Maximize Performance"
description: How to maximize performance for DeepSeek-R1 671B deployment? In this tutorial, we help you deploy DeepSeek R1 efficiently — whether you are experimenting, fine-tuning, or building cutting-edge AI applications.
image: https://company.hpc-ai.com/hubfs/1280X1280-2.png
---

[ All posts ](https://company.hpc-ai.com/blog/)

 February 14, 2025

# DeepSeek-R1 671B Deployment: How to Maximize Performance

 2 minute read

![](https://company.hpc-ai.com/hubfs/1280X1280-2.png)

**DeepSeek-R1** is the most popular AI model nowadays, attracting global attention for its impressive reasoning capabilities. It is an **open-source** LLM featuring a full CoT (Chain-of-Thought) approach for human-like inference and an MoE design that enables dynamic resource allocation to optimize efficiency. It substantially outperforms other closed-source models in a wide range of tasks including coding, creative writing, and mathematics.

![image (4)-1](https://company.hpc-ai.com/hs-fs/hubfs/image%20(4)-1.png?width=1220&height=805&name=image%20(4)-1.png) 

However, deploying a model with **671 billion parameters** requires significant computational power. Also, DeepSeek R1 demands over **1,300****GB**** of ****VRAM**** **at full weight, which is a challenge for individual users or small teams with fewer resources.

In this tutorial, we help you deploy DeepSeek R1 efficiently — whether you are experimenting, fine-tuning, or building cutting-edge AI applications. Here is how:

 

## Your Deployment Playbook: From Trial to Full-Scale Mastery

To assist in selecting the most suitable hardware configuration for your deployment, we have conducted comprehensive benchmarking on DeepSeek-R1 671B and other models within the DeepSeek series. The following table summarizes our findings:

![image (5)-2](https://company.hpc-ai.com/hs-fs/hubfs/image%20(5)-2.png?width=1816&height=1032&name=image%20(5)-2.png)

Based on our analysis, deploying the full-scale DeepSeek-R1 671B model requires a multi-GPU setup due to its extensive VRAM requirements. However, for those seeking more accessible alternatives, the distilled variants of DeepSeek-R1 offer optimized performance with reduced computational demands, making them suitable for single-GPU configurations.

 

## Full Weights: For the Bold Innovators

While you may be eager to run the full-scale **DeepSeek-R1 671B**, it is important to note that this requires distributed inference across multiple nodes. To fully unlock the potential of the DeepSeek-R1 671B, you'll need **H200 GPUs**, designed to handle trillion-parameter workloads. This is where the **H200** truly excels:

1. **Larger Memory**: Each 8×H200 pod provides **1,152GB** of VRAM (141GB × 8), allowing a single 8-card node to fully accommodate the Deepseek-R1 or Deepseek-V3 model in FP8 precision. This large memory capacity eliminates the instability of multi-node networking and enhances the reliability of model serving. 
2. **Higher ****Bandwidth**: The Chain of Thought (CoT) mechanism generates intermediate reasoning steps, increasing the demand for input and output tokens. With a memory access bandwidth of **4.8TB/s** — 1.4× higher than the H100 — the H200 is particularly well-suited for memory-intensive tasks such as language model training and inference with long context windows, while also good for multimodality models.
3. **Scalable System**: H200 clusters are designed for high-performance computing with seamless expansion, ensuring your project always has the computing power it needs.

According to benchmarking data provided by NVIDIA, the H200 achieves up to 2× the LLM inference performance of the H100 on models ranging from 13B to 175B.

![image (6)-1](https://company.hpc-ai.com/hs-fs/hubfs/image%20(6)-1.png?width=1112&height=596&name=image%20(6)-1.png)

## **Ready**** to ****Pioneer****?**

The **Dee****p****seek-R1 image** is now prebuilt and available on the **[HPC-AI.com](https://hpc-ai.com/?utm_source=hpc_blog&utm_medium=email&utm_campaign=deepseeek-r1)** platform. A detailed tutorial is provided to guide you through the process of deploying the model and serving it efficiently. To get started, go to [HPC-AI.com](https://hpc-ai.com/?utm_source=hpc_blog&utm_medium=email&utm_campaign=deepseeek-r1) to create an instance with the prebuilt Deepseek-R1 671B image.

Once your instance is running, you can load the model using vLLM. Here is an example command:

 

*vllm serve /root/commonData/DeepSeek-R1 \*  
*--host 0.0.0.0 \*  
*--port 8000 \*  
*--enable-reasoning \*  
*--reasoning-parser deepseek_r1 \*  
*--tensor-parallel-size 8 \*  
*--load-format auto \*  
*--trust-remote-code \*  
*--served-model-name deepseek-ai/DeepSeek-R1*

 

After the model is loaded, you can begin making inference requests.

Open a new terminal in the Jupyter Notebook interface and run the following command to make an inference request.

 

*curl "http://localhost:8000/v1/chat/completions" \*  
*-H "Content-Type: application/json" \*  
*-d '{*  
*"model": "deepseek-ai/DeepSeek-R1",*  
*"messages": [*  
*{*  
*"role": "system",*  
*"content": "You are a helpful assistant."*  
*},*  
*{*  
*"role": "user",*  
*"content": "Write a haiku that explains the concept of recursion."*  
*}*  
*]*  
*}'*

 

At this point, you may wish to serve the model to other users for your business needs. To do so, you can also use `curl` locally from your machine by replacing **localhost** with the instance's IP address. The instance IP address can be easily located in the **Quick Tools** section under **HTTP Ports**, where all relevant port configurations and IP addresses are displayed.

For detailed instructions, please refer to our tutorial on [Deepseek-R1 671B Inference](https://www.hpc-ai.com/doc/docs/tutorial/deepseek-inference). [HPC-AI.com](https://hpc-ai.com/?utm_source=hpc_blog&utm_medium=email&utm_campaign=deepseeek-r1) offers H200 GPUs at** $2.09 per ****GPU**** hour**. [Spin up an instance](https://hpc-ai.com/?utm_source=hpc_blog&utm_medium=email&utm_campaign=deepseeek-r1) now and begin running the Deepseek-R1 671B model.

 

[ Get started now ](https://hpc-ai.com/?utm_source=hpc_blog&utm_medium=email&utm_campaign=deepseeek-r1)

 

Reference:

1. https://www.nvidia.com/en-sg/data-center/h200/
2. Technical Report of Deepseek-R1: https://arxiv.org/pdf/2501.12948

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