# Problem with testing full capabilities of models on cloud H100. 

**URL:** <https://deeptalk.lambda.ai/t/problem-with-testing-full-capabilities-of-models-on-cloud-h100/4377>\
**Category:** Technical Help\
**Created:** [October 27, 2024, 3:11pm UTC](https://deeptalk.lambda.ai/t/problem-with-testing-full-capabilities-of-models-on-cloud-h100/4377 "2024-10-27T15:11:11Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![lukigg](https://avatars.discourse-cdn.com/v4/letter/l/d9b06d/32.png) [@lukigg](https://deeptalk.lambda.ai/u/lukigg)\
**Post date:** [October 27, 2024, 3:11pm UTC](https://deeptalk.lambda.ai/t/problem-with-testing-full-capabilities-of-models-on-cloud-h100/4377/1 "2024-10-27T15:11:11Z")

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Hi,

I created a docker image of my models and tested it on my local PC for a similar speed to be sure it work well. Then I wanted to put it on the cloud to test the speeds on H100. I use lambda labs and install everything as they described [Lambda Stack: an AI software stack that’s always up-to-date](https://lambdalabs.com/lambda-stack-deep-learning-software#server-installation) here [Set up a GPU accelerated Docker container using Lambda Stack + Lambda Stack Dockerfiles on Ubuntu 20.04 LTS](https://lambdalabs.com/blog/set-up-a-tensorflow-gpu-docker-container-using-lambda-stack-dockerfile). I had some issues but got it working but on docker, I got much higher computing times for the llama model and parler tts than on my 3090. Then I checked nvdia-smi and nvidia --version and it didn’t see GPU, so I decided to install packages through the requirements file on my cloud and just put files here as here I see gpu but got the same result. As a base image, I used Nvidia prepared image that should have everything set up and it worked on my pc [nvcr.io/nvidia/pytorch:23.08-py3](http://nvcr.io/nvidia/pytorch:23.08-py3). Am I missing something I have also conda that I used on my local PC maybe I could use it as a base so it will be better?