The choice of instance type significantly affects the cost of running DeepSeek-R1 on cloud platforms like AWS. Here's a detailed breakdown of how different instance types and configurations impact costs:
1. Instance Types and Pricing:
- AWS: The cost of running DeepSeek-R1 on AWS depends on the instance type chosen. For example, using an ml.g5.2xlarge instance provides a good balance of performance and cost for large-scale inference tasks[4]. However, for more intensive computations, larger instances like c8g.16xlarge on AWS Graviton4 can be used, which costs about $1,863 per month under On-Demand pricing[6]. This can be reduced with EC2 Savings Plans or Spot Instances, offering significant discounts for batch inference tasks.
2. Performance and Cost Optimization:
- Batch vs. Real-Time Inference: For large-scale inference, using larger batch sizes can optimize both cost and performance. Batch transform for offline inference further reduces costs by processing data in bulk rather than in real-time[4].
- Spot Instances: Utilizing Spot Instances can offer up to a 90% discount compared to On-Demand pricing, making them ideal for batch processing where interruptions are manageable[6].
3. Hardware Considerations:
- GPU vs. CPU: While GPUs like NVIDIA H100 are powerful, they are expensive. Using CPUs with ample RAM, especially for models like DeepSeek-R1 that employ Mixture of Experts (MoE) architecture, can be more cost-effective[8].
- Alternative Providers: Considering AMD MI300 nodes on Azure or tier 2 cloud providers might offer better cost-performance ratios compared to high-end GPU configurations[8].
4. Cost Reduction Strategies:
- Reserved Instances: Committing to reserved instances can provide significant discounts over On-Demand pricing for long-term usage.
- Auto Scaling: Mixing Spot Instances with On-Demand instances via Auto Scaling can balance availability and cost.
- Optimized Pricing Models: Some providers offer per-token pricing, which can be more cost-effective for intermittent AI needs compared to paying for computing resources[1].
In summary, the choice of instance type affects the cost by influencing the balance between performance and resource consumption. Optimizing instance selection based on specific workload requirements, leveraging cost-saving strategies like Spot Instances, and considering alternative hardware options can significantly reduce the overall cost of running DeepSeek-R1.
Citations:
[1] https://campustechnology.com/Articles/2025/02/04/AWS-Microsoft-Google-Others-Make-DeepSeek-R1-AI-Model-Available-on-Their-Platforms.aspx?admgarea=news
[2] https://www.linkedin.com/posts/zhu-liang_deepseek-r1-is-better-and-cheaper-wrong-activity-7288814972271280128-vuYU
[3] https://forum.effectivealtruism.org/posts/d3iFbMyu5gte8xriz/is-deepseek-r1-already-better-than-o3-when-inference-costs
[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/QU-hcixrtFSaSoKH8GL-KogA/pricing-model-of-deepseek-r1-distilled-llama-models-with-amazon-bedrock-custom-model-import
[6] https://community.aws/content/2rhRJI6cxBa1Ib5f3TjsfPadpXs/deploying-deepseek-r1-distill-llama-70b-for-batch-inference-on-aws-graviton4?lang=en
[7] https://www.reddit.com/r/LocalLLaMA/comments/1ibm5u3/how_can_we_be_so_sure_the_training_of_deepseek_r1/
[8] https://www.reddit.com/r/LocalLLaMA/comments/1icukfm/running_deepseek_r1_on_aws_cost_hardware/