Reading
Launch a temporary GPU environment at extremely low cost via AWS EC2 Spot instances, automatically deploy Ollama and Open WebUI, suitable for AI inference scenarios requiring elastic scaling, and support persistent model caching to speed up subsequent launches.
Section 01
Launch a temporary GPU environment at extremely low cost via AWS EC2 Spot instances, automatically deploy Ollama and Open WebUI, suitable for AI inference scenarios requiring elastic scaling, and support persistent model caching to speed up subsequent launches.
Section 02
Section 03
Ollama Spot Launcher is a practical AWS infrastructure tool designed to help users quickly launch temporary GPU instances at the lowest cost to run local large language models (LLMs). It fully leverages the price advantage of AWS EC2 Spot instances (usually 70-90% cheaper than on-demand instances), combined with Ollama's model services and Open WebUI's user-friendly interface, providing developers and researchers with a cost-effective elastic AI inference solution.
The core concept of this project is "launch on demand, stop after use": quickly spin up the environment when you need GPU computing power, release the instance after the task is completed, and keep the model cache on the EBS volume so that you can skip repeated downloads next time you launch, achieving readiness in minutes.
Section 04
| File | Function |
|---|---|
| launch_qwen_spot.sh | Local launch script, submits Spot/on-demand instance requests and waits for model readiness notifications |
| ec2_userdata.sh | Instance startup script, automatically installs Ollama, pulls models, and starts WebUI |
| launch.env.example | Configuration template; needs to be copied as launch.env and filled with account information |
Section 05
Section 06
The project fully utilizes the bidding mechanism of AWS Spot instances, evaluates the capacity and price of each availability zone through the Spot Placement Score API, and helps users select the optimal launch location. It also supports setting a maximum bid limit to avoid unexpected costs when prices soar.
Section 07
Under default configuration, the EBS volume is retained after the instance is terminated. This means that the pulled LLM files (usually several GB to tens of GB) are saved, and when launching a new instance next time, you only need to mount this volume to use it immediately without repeated downloads, significantly reducing startup time.
Section 08
With the --auto-az parameter, the script automatically selects the availability zone with the highest Spot Placement Score in the same VPC, eliminating the need to manually configure subnet information for multiple availability zones and simplifying the complexity of cross-zone deployment.
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