The g4dn.xlarge instance is often highlighted for its cost-performance balance, particularly for AI workloads like running DeepSeek-R1 models. Here's a detailed comparison of its performance with other instance types:
Cost-Performance Balance
- g4dn.xlarge offers a good balance between cost and performance, making it suitable for basic GPU workloads. It is priced at approximately $0.58 per hour, which is relatively affordable for many users[1][3].- In contrast, more powerful instances like g6e.xlarge or p5e.48xlarge provide higher performance but at a significantly higher cost. For example, running a 14B distilled model on a g6e.xlarge instance would cost about $880 per month, while a full DeepSeek-R1 model on a p5e.48xlarge instance could cost around $30,000 per month[7].
Performance Capabilities
- g4dn.xlarge uses NVIDIA T4 GPUs, which are mid-tier and may become a bottleneck for high-throughput applications or large-scale deployments[6]. However, for smaller-scale or development environments, it provides sufficient performance.- For higher performance and efficiency, instances like Inf2.xlarge or Inf2.8xlarge, which use AWS Inferentia chips, are recommended. These instances offer better scalability and lower latency but are more expensive[6].
Memory and Resource Requirements
- DeepSeek-R1 models can require a wide range of memory, from 1.1 GB to 404 GB depending on the specific use case[9]. The g4dn.xlarge instance is suitable for smaller models or less memory-intensive tasks, while larger instances like p4d.24xlarge are needed for more demanding applications.Scalability and Flexibility
- AWS allows for easy scalability of instances based on demand. If DeepSeek-R1 requires more resources, users can upgrade to larger instances or add more instances in a cluster[3]. This flexibility is crucial for projects that need to process large volumes of data or handle variable workloads.In summary, the g4dn.xlarge instance is a cost-effective choice for running DeepSeek-R1 models, especially for smaller-scale applications or development environments. However, for larger models or production environments requiring high throughput and low latency, more powerful instances like those in the Inf2 or p5e series are preferable despite their higher costs.
Citations:
[1] https://www.reddit.com/r/LocalLLaMA/comments/1dclmwt/benchmarking_inexpensive_aws_instances/
[2] https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1
[3] https://community.aws/content/2sHGS4Eqeekz32OOzn7am5lnGEX/benefits-of-installing-deepseek-on-an-aws-ec2-instance?lang=en
[4] https://www.pulumi.com/blog/run-deepseek-on-aws-ec2-using-pulumi/
[5] https://www.datacamp.com/blog/deepseek-r1
[6] https://plusai.com.au/blog/things-to-consider-before-deploying-deepseek
[7] https://community.aws/content/2Z6DlAohx12yuNoEAs7qb5YTH0q/leveraging-deepseek-r1-on-aws?lang=en
[8] https://huggingface.co/deepseek-ai/DeepSeek-R1
[9] https://irensaltali.com/setting-up-ec2-for-deepseek-r1-with-nvidia-drivers-and-typingmind-integration/