This content originally appeared on DEV Community and was authored by Agam Jain
Hi Everyone
Deepseek-R1 is everywhere. So, we have done the heavy lifting for you to run each variant on the cheapest and highest-availability GPUs. All these configurations have been tested with vLLM for high throughput and auto-scale with the Tensorfuse serverless runtime.
Below is the table that summarizes the configurations you can run.
Take it for an experimental spin
You can find the Dockerfile and all configurations in the GitHub repo below. Simply open up a GPU VM on your cloud provider, clone the repo, and run the Dockerfile.
Github Repo: https://github.com/tensorfuse/tensorfuse-examples/tree/main/deepseek_r1
Deploy a production-ready service on AWS using Tensorfuse
If you are looking to use Deepseek-R1 models in your production application, follow our detailed guide to deploy it on your AWS account using Tensorfuse.
The guide covers all the steps necessary to deploy open-source models in production:
- Deployed with the vLLM inference engine for high throughput
- Support for autoscaling based on traffic
- Prevent unauthorized access with token-based authentication
- Configure a TLS endpoint with a custom domain
This content originally appeared on DEV Community and was authored by Agam Jain
Agam Jain | Sciencx (2025-01-29T20:03:47+00:00) Deploy Deepseek-R1: Guide to run multiple variants on AWS. Retrieved from https://www.scien.cx/2025/01/29/deploy-deepseek-r1-guide-to-run-multiple-variants-on-aws/
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