How to Set Up and Run Ollama on a GPU-Powered VM

AI-powered model inference on GPU-enabled VMs offers enhanced performance and data security for machine learning projects, with Ollama serving as a key tool for private model deployment.

Setting up the environment: Establishing a GPU-powered virtual machine on Vast.ai is the first step in leveraging Ollama for private model inference.

• Choose a VM with at least 30 GB of storage to accommodate model files and ensure sufficient space for installation.
• Select a cost-effective option, aiming for a VM that costs less than $0.30 per hour.
• After VM setup, initiate Jupyter and open a terminal within it for ease of access and configuration.

Installing and running Ollama: The process of getting Ollama up and running involves a few straightforward steps to ensure proper functionality.

• Install Ollama by executing the command curl -fsSL https://ollama.com/install.sh | sh in the Jupyter terminal.
• Start the Ollama service with the command ollama serve &, ensuring there are no GPU errors during this process.
• Test the setup by running a sample model, such as Mistral, using the command ollama run mistral.

Verifying GPU utilization: Confirming that the GPU is being properly utilized is crucial for optimal performance of the model inference tasks.

• During the inference process, check GPU usage by running the nvidia-smi command.
• Ensure that the GPU memory utilization is greater than 0%, indicating active use of the GPU for inference tasks.

Integrating custom Hugging Face models: Ollama also supports the use of custom models from Hugging Face, expanding its versatility for specific use cases.

• Install the Hugging Face CLI using pip to enable model downloads from the platform.
• Download the desired model using the Hugging Face CLI, specifying the model name and destination directory.
• Create a model configuration file (Modelfile) to define parameters and system messages for the custom model.
• Use Ollama to create and run the custom model based on the configuration file.

Broader implications: The ability to run AI models privately on GPU-powered VMs represents a significant advancement in balancing performance and data security for machine learning applications.

• This approach allows organizations to maintain control over sensitive data while still leveraging the power of advanced AI models.
• The flexibility to use both pre-existing and custom models enhances the potential applications across various industries and use cases.
• As AI continues to evolve, tools like Ollama that facilitate private, efficient model deployment will likely play an increasingly crucial role in the AI ecosystem.