8/27/2024

Running Ollama on NVIDIA GPUs

Are you curious about leveraging the POWER of NVIDIA GPUs to run Ollama? If you've dabbled in Large Language Models (LLMs), you might know that executing these models on a capable GPU can significantly enhance performance. In this detailed blog post, we’ll guide you through everything you need to know about setting up Ollama on NVIDIA GPUs. Let’s dive into the nitty-gritty!

What is Ollama?

Ollama is an open-source framework that simplifies running large language models locally. Using Ollama, you can create and interact with these sophisticated models in your own environment without needing to rely on external API calls. This not only maximizes control over your data but also provides the flexibility to tweak the models to suit your needs. If you want to learn more about Ollama, check out their official blog.

Why Use NVIDIA GPUs?

NVIDIA GPUs are the heavyweight champions when it comes to handling demanding computational tasks, particularly in the realms of machine learning & AI. Here are a few GREAT reasons why you should utilize NVIDIA GPUs with Ollama:

High Performance: NVIDIA’s architecture is built for parallel processing, making it perfect for training & running deep learning models more efficiently.
CUDA Support: Ollama supports CUDA, which is optimized for NVIDIA hardware. This leads to faster computing & reduced run-time.
Wide Compatibility: Ollama is compatible with various GPU models, and NVIDIA's extensive range of products ensures you'll find one that suits your budget & performance needs.

Getting Started: Requirements for Ollama on NVIDIA GPUs

Before we jump into the installation wizardry, let’s lay out some recommendations & prerequisites:

System Requirements

As per Ollama’s documentation, here's what you'll need:

Operating System: Must be Linux (Ubuntu 18.04 or later) or macOS (Big Sur and later).
RAM: At least 8GB for 3B models, 16GB for 7B models, and 32GB for 13B models.
Disk Space: Expect to require at least 12GB for base installations, more for model weights.
GPU: should have a compute capability of 5.0 or higher for proper functionality. You can check your GPU's compute capability through the NVIDIA documentation.
Supported NVIDIA GPU Models
NVIDIA supports a vast range, but here are some popular models you might consider:
RTX 4090 (Compute Capability 8.9)
RTX 3080 (Compute Capability 8.6)
GTX 1050 Ti (Compute Capability 5.0) If you're unsure whether your GPU meets requirements, you can check the full list on NVIDIA’s CUDA GPUs page.

Installing NVIDIA Drivers

Getting your NVIDIA drivers set up correctly can feel like an uphill battle. However, it’s vital to ensure you have compatible drivers.

. First, ensure you have the latest version of the NVIDIA drivers. You can check for the latest drivers through the official NVIDIA driver downloads page.
Install the drivers using package management commands (e.g.,
1sudo apt-get install nvidia-driver
for Ubuntu).
Finally, after installation, restart your machine for the drivers to take effect.

After installation, you can check whether NVIDIA is recognized using the command:

1
2

bash
nvidia-smi

It should list your GPU statistics & confirm that the drivers are installed correctly.

Installing Ollama

To get Ollama running, all it takes is 1 command! Run the following in your terminal:

1
2

bash
curl -fsSL https://ollama.ai/install.sh | sh

This command downloads & installs Ollama, configuring necessary components automatically.

Once installed, you can check the version to confirm it’s up & running:

1
2

bash
ollama --version

Configuring Ollama for GPU Use

Now that you have installed Ollama and your NVIDIA drivers are functioning, it’s time to ensure Ollama utilizes your GPU effectively. This generally involves setting the GPU in your system settings and sometimes tweaking the software settings as well:

Check Windows Graphics Settings:
- If you're on Windows, ensure you set Ollama to run using NVIDIA GPU in graphics settings. Open Settings > System > Display > Graphics settings.
- Select the Ollama executable and set it to high performance using NVIDIA.
Setup CUDA_VISIBLE_DEVICES in your environment:
- If you have multiple GPUs, you might want to specify which ones to use. Export the desired GPU IDs:
  1 2bash export CUDA_VISIBLE_DEVICES=0,1
  You replace
  10,1
  with the IDs of the GPUs you want to use.
Configure Ollama’s Model File:
- Understanding & setting the configuration details in your model file is essential. For advanced performance, allocate resources efficiently using parameters like
  1--main-gpu
  when running your commands.

Running Models with Ollama

To run models using Ollama with your NVIDIA GPU, simply use the commands:

1
2

bash
ollama run <your_model>

This command loads your model utilizing configured GPU resources under the hood. You can monitor the GPU utilization during the run using

nvidia-smi

, which allows you to see how much memory your model consumes while running.

Example: Running Llama2 Model

To specifically run the popular Llama2 model:

1
2

bash
ollama run llama2

This will employ your GPU for processing, reducing response time significantly compared to running it on CPU alone.

Troubleshooting Common Issues

Sometimes, the setup doesn't go as smoothly as expected. Here are some common troubleshooting tips:

Ollama Doesn’t Detect GPU: Double-check your driver installations. Ensure the correct versions of CUDA & NVIDIA are installed & compatible with your version of Ollama. The command
1nvidia-smi
should help you monitor your GPU and driver versions.
Slow Performance: Ensure that your model configuration is using the correct GPU settings. If it still underperforms, consider upgrading your hardware or optimizing the model configurations further.
Multiple GPUs Not Utilized: Use the
1CUDA_VISIBLE_DEVICES
environment variable to force using specified GPUs only for Ollama commands, especially helpful in a multi-GPU setup.

Conclusion

Running Ollama on NVIDIA GPUs opens up a RADICAL new level of performance for local large language models. With the ability to leverage GPU acceleration, Ollama enables high-throughput processing, making it IDEAL for various machine learning tasks. As the technology keeps advancing, tools like Ollama that optimize local execution will certainly become a game-changer in how developers & researchers work with AI.

If you want to dive deeper into building your own chatbots and enhancing your online engagement, check out Arsturn. With a simple no-code solution, you can just go from 0 to chatbot hero in no time! Discover how effortlessly you can build & customize your own AI-powered chatbots at Arsturn. Claim your free trial today without any credit card requirements and start engaging your audience more effectively!