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

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

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

8. Validate that all Nvidia H100 GPUs are visible

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

http://localhost:8080

11. Select the model from the Open WebUI

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

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.