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Benefits of installing DeepSeek on an AWS EC2 instance

Benefits of installing DeepSeek on an AWS EC2 instance

"Installing DeepSeek on an AWS EC2 instance offers a number of significant benefits for accelerating language models and machine learning.

Published Jan 28, 2025
With DeepSeek, you can speed up inference as DeepSeek leverages the GPU architecture of EC2 instances to accelerate language model inference, significantly reducing response times.
Using DeepSpeed ​​technology, DeepSeek optimizes instance resource usage, allowing for higher performance with fewer resources, EC2 instances offer a wide range of scalability options, allowing you to fine-tune resources based on your model's needs, and best of all is the integration with AWS, allowing for easy instance configuration and management, as well as access to other AWS services for increased functionality.
Installing DeepSeek on an AWS EC2 instance offers a scalable, efficient, and cost-effective solution for accelerating language model inference and more on an AWS G4 EC2 instance that can offer us several Benefits:
1. GPU Acceleration (NVIDIA T4)
• G4 instances are equipped with NVIDIA T4 GPUs, which are ideal for AI, ML, and data-intensive workloads.
• DeepSeek can leverage these GPUs to accelerate tasks such as model inference, neural network training, and parallel data processing.
2. Cost Optimization
• G4 instances offer a good balance between performance and cost, especially for AI workloads.
• By using DeepSeek on a G4 instance, you can optimize resource usage and reduce processing time, resulting in savings on AWS billing.
3. On-Demand Scalability
• AWS allows you to scale G4 instances based on your needs. If DeepSeek requires more resources for specific tasks, you can increase the instance size or add more instances in a cluster.
• This is especially useful for projects that require processing on large volumes of data.
4. AI/ML Framework Support
• DeepSeek can integrate with popular frameworks such as TensorFlow, PyTorch, or MXNet, which are optimized to run on NVIDIA GPUs.
• G4 instances support these frameworks, making it easier to deploy complex models.
5. Low Latency Performance
• T4 GPUs are designed to deliver efficient performance for real-time inference tasks.
• If DeepSeek is used for applications that require fast responses (such as chatbots, natural language processing, or image analysis), the G4 instance ensures low latency performance.
6. Container and Kubernetes Support
• G4 instances support Docker and Kubernetes, making it easier to deploy DeepSeek in container environments.
• This allows for more efficient resource management and greater software portability.
7. Energy Savings
• T4 GPUs are designed to be energy efficient, which reduces power consumption compared to other high-end GPUs.
• This translates into lower operating costs and a lower environmental impact.
8. Integration with AWS Services
• By using DeepSeek on a G4 instance, you can easily integrate it with other AWS services, such as:
o Amazon S3 for data storage.
o AWS Lambda for serverless function execution.
o Amazon SageMaker for ML model training and deployment.
• This allows you to create complete, automated workflows.
9. Security and Compliance
• AWS offers built-in security tools, such as VPC, IAM, and data encryption, that you can use to protect your DeepSeek deployment.
• This is crucial if you are handling sensitive data or complying with regulations such as GDPR or HIPAA.
10. Flexibility for multiple workloads
• G4 instances are not only useful for AI/ML, but also for other workloads such as graphics rendering, video streaming, and high-performance applications.
• DeepSeek can adapt to these needs, taking advantage of the versatility of the instance.
Below I leave you the installation with the commands so that you can make the most of your instance:
first install Ollama
https://ollama.com/
sudo ufw allow 11434/tcp
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sudo apt update && sudo apt upgrade
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sudo apt install curl
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look for the version
curl --version
in Ollama you click on download and copy the installation url
the one that says install with command
paste and enter
in the ollama search engine you look for deepseek-r1
there are several models there but you choose the smallest 1.5 million parameters.
A parameter (also known as weight or coefficient) is a numerical value that is used to adjust the output of a neuron or a layer of the neural network.
You can put 14b
When you select it, it gives you a run, you copy it
and replace the run with pull
ollama pull deepseek-r1:14b
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Since it is 14 gigas, it can take a few minutes
pip list
ollama list
ollama run deepseek-r1:14b
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You ask the question
>>>How many inhabitants does Brazil have?
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Answer here.
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The importance of using resources in combination with AWS tools and models greatly benefits organizations.
Enrique Aguilar Martinez
AI Enginer

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