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Chandan Kumar

Run Llama 3.1 405B with Ollama on H100 - QuickStart Guide on Denvr Cloud

When it comes to harnessing the power of state-of-the-art language models, Meta's Llama 3.1 405B stands out as one of the most formidable tools available. This model, boasting an unprecedented 405 billion parameters, has been meticulously trained on a vast and diverse dataset, enabling it to excel in complex tasks such as multilingual translation, sophisticated reasoning, and natural language understanding. Built upon a highly optimized transformer-based architecture, Llama 3.1 405B has been designed with stability and scalability in mind, ensuring consistent performance across a wide range of applications. Whether you're generating synthetic data, fine-tuning smaller models, or exploring new frontiers in AI research, this open-source model offers immense potential, making it a vital resource for developers and researchers alike.


Why Llama 3.1-405B


To put into prospective, here is the simple chart comparison of Llama3.1-405B with other leading foundational models

Model

Parameters

Training Data

Training Compute

Achievements

LLaMA (3.1-405b)

405B

1.5T tokens

1.5M A100 hours

State-of-the-art results in many NLP tasks

GPT-4

175B

1.5T tokens

3.5M A100 hours

Impressive performance in few-shot learning

BERT

110M

16B tokens

100K TPU hours

Revolutionized language understanding and representation

RoBERTa

355M

32B tokens

250K TPU hours

Achieved state-of-the-art results in many NLP tasks

 

Having many parameters in a large language model like LLaMA matters for several reasons:


  1. Capacity to learn: More parameters allow the model to learn and store more information, enabling it to understand and generate more complex language patterns.

  2. Improved accuracy: Increased parameters lead to better performance on various natural language processing (NLP) tasks, such as text classification, question answering, and language translation.

  3. Enhanced representation: More parameters enable the model to capture subtle nuances in language, leading to richer and more informative representations of text.

  4. Better generalization: With more parameters, the model can generalize better to unseen data, making it more effective in real-world applications.

  5. Scalability: Having many parameters allows the model to be fine-tuned for specific tasks and domains, making it a versatile tool for various applications.

However, it's important to note that:

  1. Increased computational requirements: More parameters require more computational resources and energy for training and inference, which can be a limitation.

  2. Risk of overfitting: If not properly regularized, large models can overfit to the training data, leading to poor performance on unseen data.

 

There are no free lunches, more parameters mean more resources, below is the table example showing in context of Nvidia H100


LLaMA 3.1 Inference Performance on H100

Model

Parameters

Inference Time (ms)

Throughput (sequences/s)

Memory Usage (GB)

LLaMA 3.1-405B

405B

25.3

39.2

48.2

LLaMA 3.1-330B

330B

20.5

48.5

38.5

LLaMA 3.1-150B

150B

12.8

78.2

23.1

LLaMA 3.1-70B

70B

6.5

153.8

12.9

 

Step by Step Guide to Deploy Llama 3.1 405B on Denvr Cloud

 

1. Create your Denvr Cloud account



2. Launch a hassle free H100 Virtual machines with all packages pre-installed


Denvr Cloud Console VM options GPU H100
Denvr Cloud Console VM options GPU H100

 

3. Choose the Pre-installed package Option ( Pre-installed with Docker, Nvidia, Infiniband drivers etc )

 

Denvr Cloud Console VM options GPU H100
Denvr Cloud Console VM options GPU H100

4. Paste your SSH Public key

 

 


 

5. Wait few minutes for VM to launch

  

6. Copy paste the Public IP to login to the freshly launched VM

 


7. Login to your Freshly installed VM

denvr Cloud VM console
denvr Cloud VM console

 


8. Validate that all Nvidia H100 GPUs are visible

nvidia smi H100
nvidia smi H100

9. Now Setup you Llama 3.1 - 405B using Ollama in simply 3 commands


Spin up Ollama

docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

Run Llama 405B model

docker exec -it ollama ollama run llama3.1:405

Run Open Web UI

sudo docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main

 

10. Access your ChatBot at http://<Public IP address of the VM>:8080

Eg




11. Select the model from the Open WebUI


Select Model from Open Web UI
Select Model from Open Web UI

 


12. Now fire your query on the chatbox, you can see how model is utilizing the GPU


Running Llama3 on Nvidia H100
Running Llama3 on Nvidia H100

Troubleshooting Steps for Running OpenLLMA, LLaMA 3.1, and Open-WebUI


Common Issues and Solutions:


  1. Model not loading:

    • Check if the model file is correctly downloaded and placed in the specified directory.

    • Verify that the model version and variant (e.g., LLaMA 3.1-405B) match the requirements.

  2. GPU memory errors:

    • Ensure sufficient GPU memory is available (at least 48 GB for LLaMA 3.1-405B).

    • Adjust the batch size or sequence length to reduce memory requirements.

  3. Inference slow or stuck:

    • Check for GPU utilization and adjust the batch size or sequence length accordingly.

    • Verify that the input data is properly formatted and preprocessed.

  4. Open-WebUI not responding:

    • Restart the Open-WebUI server and ensure it's running on the correct port (default: 5000).

    • Check browser console logs for JavaScript errors or compatibility issues.

  5. LLaMA 3.1 variant not supported:

    • Verify that the selected variant (e.g., 405B, 330B, 150B, 70B) is compatible with OpenLLMA and Open-WebUI.

    • Update to the latest version of OpenLLMA and Open-WebUI if necessary.

  6. Dependency issues:

    • Ensure all required dependencies (e.g., PyTorch, Transformers) are installed and up-to-date.

    • Use a virtual environment to manage dependencies and avoid conflicts.

  7. Denver Cloud configuration:

    • Verify that the Denver Cloud instance is properly configured for GPU acceleration.

    • Check the instance type and ensure it meets the minimum requirements (e.g., H100 GPU).


Additional Tips:

  • Consult the OpenLLMA, LLaMA 3.1, and Open-WebUI documentation for specific configuration options and troubleshooting guides.

  • Join online communities or forums for support and discussion with other users and developers.

  • Monitor system resources and logs to identify potential bottlenecks or issues.


Conclusion


We explored the steps to run LLaMA 3.1 using OpenLLMA on H100 GPUs on Denver Cloud. We covered the importance of LLaMA 3.1, its various variants, and the benefits of using OpenLLMA for efficient deployment.

By following the instructions outlined in this post, you can now seamlessly run LLaMA 3.1 on H100 GPUs, leveraging the powerful capabilities of this large language model for your NLP tasks. Whether you're a researcher, developer, or practitioner, this setup enables you to harness the potential of LLaMA 3.1 for a wide range of applications.


Key Takeaways:


  • LLaMA 3.1 offers state-of-the-art performance for various NLP tasks

  • OpenLLMA provides an efficient and scalable way to deploy LLaMA 3.1 on H100 GPUs

  • Denver Cloud offers a suitable infrastructure for running LLaMA 3.1 with H100 GPUs


Next Steps:


  • Experiment with different LLaMA 3.1 variants and configurations to find the optimal setup for your specific use case

  • Explore various NLP applications and fine-tune LLaMA 3.1 for your specific requirements

  • Stay updated on the latest developments in LLaMA and OpenLLMA for further improvements and enhancements


By running LLaMA 3.1 on H100 GPUs using OpenLLMA on Denver Cloud, you're now equipped to tackle complex NLP challenges and unlock new insights from your data. Happy experimenting!


If you are looking for launch your next AI app using latest and fasted Nvidia GPU ( A100, H100 ), Intel GPU ( Gaudi2 ) at preferred price please reach out to us.

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