ollama-k80-lab
Overview
This project explores running Ollama, a local LLM runner, with a NVIDIA K80 GPU and investigates its integration with Dify, a powerful framework for building LLM-powered applications. The goal is to assess performance, explore limitations, and demonstrate the potential of this combination for local LLM experimentation and deployment.
Motivation
- Local LLM Exploration: Ollama makes it incredibly easy to run Large Language Models locally. This project aims to leverage that ease with the power of a GPU.
- K80 Utilization: The NVIDIA K80, while older, remains a viable GPU for LLM inference. This project aims to demonstrate its capability for running smaller to medium sized LLMs.
- Dify Integration: Dify provides a robust framework for building LLM applications (chatbots, agents, etc.). We want to see how seamlessly Ollama and Dify can work together, allowing us to rapidly prototype and deploy LLM-powered solutions.
- Cost-Effective Experimentation: Running LLMs locally avoids the costs associated with cloud-based APIs, enabling broader access and experimentation.
Modified Version
This repository includes a modified version of Ollama, specifically customized for running on a Tesla K80 GPU. For more details and contributions, visit our GitHub page:
This custom build aims to optimize performance and compatibility with the Tesla K80 hardware, ensuring smoother integration and enhanced efficiency in LLM applications.
Video Showcase
Check out this video showcasing "DeepSeek-R1:32b in Action on Tesla K80 GPU - Real-Time Performance Showcase":
Description: Whether you’re a developer looking to optimize AI models on similar hardware, or just curious about high-performance computing setups, this video offers valuable insights. From technical setup tips to performance benchmarks, we cover it all.
What You'll See:
- NVIDIA-SMI Status
- Ollama Log Insights
- Real-Time Response Time Analysis
License
This project is licensed under the MIT License.