Optimizing the performance of DeepSeek-R1 on AWS involves several network settings and configurations. Here's a detailed overview of how to enhance its performance:
1. EC2 Instance Configuration**
- Instance Type: Choosing an instance type with sufficient GPU power, such as `g4dn.xlarge`, is crucial for running DeepSeek-R1 models efficiently. This instance type supports NVIDIA GRID drivers, which are essential for GPU acceleration[1].- Network Settings: Use the default VPC settings and select the availability zone where your EC2 instance is deployed. This ensures that your instance is well-connected and accessible within your network[1].
2. Security Groups**
- Configure a new security group with specific inbound rules:- HTTP Traffic: Allow HTTP traffic from a trusted IP range (e.g., "My IP") to enable web access to the model.
- TCP Traffic on Port 3000: Allow TCP traffic from the VPC CIDR range to facilitate communication with the Application Load Balancer.
- HTTPS Traffic: Allow HTTPS traffic from the VPC CIDR range for secure communication[1].
3. Application Load Balancer (ALB)**
- Scheme: Set up an internet-facing ALB to expose your DeepSeek-R1 model to external traffic.- Load Balancer IP Address Type: Use IPv4 for simplicity and compatibility.
- Network Settings: Select the default VPC settings and the same availability zone as your EC2 instance.
- Security Groups: Use the security group created during EC2 configuration to ensure consistent access controls[1].
4. Target Group Configuration**
- Target Type: Select "Instances" as the target type.- Port: Use port 3000 to forward traffic to the EC2 instance running the DeepSeek-R1 model.
- Target Group Name: Name the target group (e.g., "deepseek-tg") for easy identification[1].
5. Amazon SageMaker for Enhanced Performance**
- Model Deployment: Consider deploying DeepSeek-R1 models using Amazon SageMaker, which offers features like auto-scaling and elastic load balancing. This can improve responsiveness and scalability[3][4].- Private S3 Bucket: Store model weights in a private S3 bucket to reduce latency and enhance security by keeping model data within your AWS account[3].
6. Optimization Best Practices**
- Prompt Optimization: Use techniques like prompt optimization on Amazon Bedrock to enhance the reasoning capabilities of DeepSeek-R1 models[7].- Region Selection: Choose an AWS region closest to your users to minimize latency and optimize costs[6].
By implementing these network settings and configurations, you can optimize the performance of DeepSeek-R1 on AWS, ensuring efficient, scalable, and secure model deployment.
Citations:
[1] https://community.aws/content/2sEuHQlpyIFSwCkzmx585JckSgN/deploying-deepseek-r1-14b-on-amazon-ec2?lang=en
[2] https://northflank.com/blog/self-host-deepseek-r1-on-aws-gcp-azure-and-k8s-in-three-easy-steps
[3] https://aws.amazon.com/blogs/machine-learning/optimize-hosting-deepseek-r1-distilled-models-with-hugging-face-tgi-on-amazon-sagemaker-ai/
[4] https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-models-on-amazon-sagemaker-using-a-large-model-inference-container/
[5] https://repost.aws/questions/QUzC1_jMmESBmpAuOzQh5JcA/guidance-on-aws-deepseek-ai-pricing-and-deployment-options
[6] https://crossasyst.com/blog/deepseek-r1-on-aws-bedrock/
[7] https://aws.amazon.com/blogs/machine-learning/optimize-reasoning-models-like-deepseek-with-prompt-optimization-on-amazon-bedrock/
[8] https://aws.amazon.com/blogs/aws/deepseek-r1-models-now-available-on-aws/