Comparing the latency of DeepSeek-R1 with OpenAI's models on Amazon SageMaker involves examining several factors, including model architecture, deployment strategies, and hardware configurations.
DeepSeek-R1 Latency
DeepSeek-R1 is known for having a higher latency compared to average models. It takes approximately 9.71 seconds to receive the first token (Time to First Token, TTFT) in some configurations[7]. This latency can be attributed to the model's complex reasoning capabilities and its "thinking phase," which involves processing before generating responses[3]. DeepSeek-R1 distilled models, however, offer more efficient alternatives by reducing computational overhead while maintaining much of the original model's reasoning capabilities[9].
On SageMaker, DeepSeek-R1's performance can be optimized using strategies like speculative decoding and model sharding across multiple GPUs, which can help decrease latency and improve throughput[1]. The use of Hugging Face's Transformers and SageMaker's automatic load balancing and autoscaling features also enhance deployment efficiency[5].
OpenAI Models Latency
OpenAI's models, such as the o1 model, are generally faster than DeepSeek-R1. The o1 model is nearly twice as fast at generating answers, indicating that it spends less time in the "thinking phase"[3]. However, specific latency figures for OpenAI models on SageMaker are not detailed in the available information. OpenAI models are typically optimized for speed and responsiveness, making them suitable for real-time applications.
SageMaker Deployment Considerations
Amazon SageMaker provides tools to optimize latency for both DeepSeek-R1 and OpenAI models. Strategies like the Least Outstanding Requests (LOR) routing strategy can minimize latency by efficiently distributing incoming requests across instances based on their capacity and utilization[2]. Additionally, using optimized hardware configurations and leveraging DeepSpeed or similar technologies can further reduce inference latency[6].
In summary, while DeepSeek-R1 has higher latency compared to OpenAI models, its distilled variants and optimized deployment strategies on SageMaker can improve performance. OpenAI models are generally faster but may come at a higher cost compared to DeepSeek-R1's more cost-effective options[3][8].
Citations:
[1] https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-models-on-amazon-sagemaker-using-a-large-model-inference-container/
[2] https://aws.amazon.com/blogs/machine-learning/minimize-real-time-inference-latency-by-using-amazon-sagemaker-routing-strategies/
[3] https://www.vellum.ai/blog/analysis-openai-o1-vs-deepseek-r1
[4] https://arstechnica.com/ai/2025/01/how-does-deepseek-r1-really-fare-against-openais-best-reasoning-models/
[5] https://aws.amazon.com/blogs/machine-learning/optimize-hosting-deepseek-r1-distilled-models-with-hugging-face-tgi-on-amazon-sagemaker-ai/
[6] https://aws.amazon.com/blogs/machine-learning/how-mantium-achieves-low-latency-gpt-j-inference-with-deepspeed-on-amazon-sagemaker/
[7] https://artificialanalysis.ai/models/deepseek-r1
[8] https://365datascience.com/trending/deepseek-vs-openai/
[9] https://www.flotorch.ai/blog/speed-up-rag-experiments-on-aws-sagemaker-with-deepseek-r1-flotorch
[10] https://pages.awscloud.com/rs/112-TZM-766/images/2023_VW-0616-MCL_Slide-Deck.pdf
[11] https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1