Monitoring and evaluating the performance of DeepSeek-R1 on AWS GPU instances involves several steps and tools. Here's a detailed guide on how to do it effectively:
Monitoring Tools and Metrics
Amazon CloudWatch
Amazon CloudWatch is a key tool for monitoring AWS resources, including GPU instances. It collects and processes raw data into readable, near real-time metrics. For GPU instances, you can monitor metrics such as:- GPU Utilization: The percentage of allocated GPUs currently in use.
- GPUMemoryUtilization: The percentage of total GPU memory in use.
- CPUUtilization: The percentage of allocated EC2 compute units in use.
- MemoryUtilization: The percentage of memory used during the sample period.
To enable GPU utilization metrics, you need to install the CloudWatch agent on your instances and configure it to collect NVIDIA GPU metrics[2][8].
NVIDIA Metrics
In addition to CloudWatch metrics, you can use the `nvidia-smi` command to monitor GPU performance in real time. This command provides detailed information about GPU utilization, memory usage, and temperature[5].Performance Evaluation Metrics for DeepSeek-R1
When evaluating the performance of DeepSeek-R1 models, focus on the following metrics:
- End-to-End Latency: The time between sending a request and receiving the response.
- Throughput (Tokens per Second): The number of tokens processed per second.
- Time to First Token: The time taken to generate the first token in a response.
- Inter-Token Latency: The time between generating each token in a response[1][4].
Scenarios for Testing
To evaluate DeepSeek-R1 performance effectively, consider testing different scenarios:- Input Token Lengths: Test with short (e.g., 512 tokens) and medium (e.g., 3072 tokens) input lengths to assess how the model handles varying input sizes.
- Concurrency Levels: Evaluate performance under different concurrency levels (e.g., 1, 10) to assess scalability.
- Hardware Configurations: Use various GPU instance types (e.g., p4d, g5, g6) with different numbers of GPUs to find the optimal configuration for your workload[1][4].
Best Practices for Monitoring and Evaluation
- Use Amazon SageMaker: Deploy DeepSeek-R1 models using SageMaker to leverage its managed infrastructure and performance monitoring capabilities.- Custom Testing: Perform custom testing with your specific datasets and use cases to ensure the results are relevant to your application.
- Continuous Monitoring: Regularly monitor performance metrics to identify bottlenecks and optimize resource utilization[4][7].
By following these steps and using the right tools, you can effectively monitor and evaluate the performance of DeepSeek-R1 on AWS GPU instances.
Citations:
[1] https://aws.amazon.com/blogs/machine-learning/optimize-hosting-deepseek-r1-distilled-models-with-hugging-face-tgi-on-amazon-sagemaker-ai/
[2] https://docs.aws.amazon.com/compute-optimizer/latest/ug/ec2-metrics-analyzed.html
[3] https://www.reddit.com/r/singularity/comments/1ik2zf6/you_can_now_train_your_own_deepseekr1_model_on/
[4] https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-models-on-amazon-sagemaker-using-a-large-model-inference-container/
[5] https://www.cloudoptimo.com/blog/aws-g6-gpu-instances-performance-use-cases-and-cost-optimization/
[6] https://github.com/aws-samples/amazon-sagemaker-generativeai/blob/main/llm-performance-evaluation/deepseek-r1-distilled/deepseek-r1-distilled-performance-evaluation-report.ipynb
[7] https://virtualizationreview.com/Articles/2025/03/11/AWS-First-Cloud-Giant-to-Offer-DeepSeek-R1-as-Fully-Managed-Serverless-Model.aspx
[8] https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/CloudWatch-Agent-NVIDIA-GPU.html
[9] https://aws.amazon.com/about-aws/whats-new/2025/03/deepseek-r1-fully-managed-amazon-bedrock/