Host and Run Open-source LLMs: Exploring Ollama, vLLM, Hugging Face

Simplifying Hosting and Running Open-source LLMs with the Right Tools

Whether you are using a proprietary or open-source large language model, you need some level of LLMOps. LLMOps is a set of practices focused on making the deployment, maintenance, and scaling of LLMs efficient, cost-effective, and sustainable in real-world applications.

For Proprietary or Closed LLMs, the model-providing companies majorly handle LLMOps tasks like deployment, scaling, maintenance, and monitoring as part of their service offerings. And the user must manage tasks like integrating the LLM’s APIs into their applications and managing how the models are used in specific workflows. Other LLMOps tasks that the user should perform include, monitoring costs based on their usage patterns, handling the scaling of the application when the LLM scales, and ensuring their application stays compatible with the new versions of LLM.

Contrarily, for Open-source LLMs, you must handle the model’s entire lifecycle, including its deployment, scaling, maintenance, monitoring, fine-tuning, and optimization. The greater responsibility of handling end-to-end LLMOps to manage open-source LLMs can feel daunting but it doesn't have to be if you use the right tools. We’ll discuss three tools popularly used to host and run LLMs locally- Ollama, vLLM, and Hugging Face.

Image showing open source LLM hosting tools Ollama, vLLM, Hugging Face

Ollama: Deploy and Manage Open-source LLMs Locally

Ollama is an open-source tool or platform that simplifies the deployment, management, and scaling of LLMs locally. It provides a lightweight, user-friendly framework that allows running LLMs like Llama, Phi, Mistral, etc., efficiently without requiring extensive infrastructure or cloud dependencies. This eliminates the complexities and costs associated with cloud-based solutions and gives you complete control over data privacy and security.

Key Features of Ollama

Complete Management of LLMs Locally: Ollama provides complete control over open-source LLMs, allowing users to easily download, update, and delete them directly on their system. This helps maintain strict control over data security. It also enables tracking and managing different versions of LLMs in both research and production settings. This helps to test or roll back to specific versions to achieve the best outcomes or troubleshoot issues.

Flexible Control with Command-Line and GUI Options: This tool utilizes a command-line interface (CLI), offering users fine-grained control over their LLMs. So, you can swiftly execute commands to download, run, and manage models, making it a great option if you prefer working directly in the terminal. It also integrates with third-party graphical user interfaces (GUIs), like Open WebUI which helps you interact with models through an intuitive interface, providing an alternative if you favor a more visual approach and want a simpler, more user-friendly experience.

Seamless Multi-Platform Support: It supports multiple operating systems like macOS, Linux, and Windows to fit into your existing workflows, irrespective of the platform you use. Thanks to Linux compatibility, you can install it on a virtual private server (VPS) for remote access and management. So, it provides more scalability to large-scale projects or teams that need to collaborate across different locations.

Ollama can be utilized for versatile use cases like:

vLLM: Optimize and Scale Open-source LLMs

vLLM (Virtual Large Language Model) is an open-source library that has evolved into a community-driven project designed for high-performance hosting and running of LLMs. It excels at running large models at scale, with efficient memory management and the ability to handle multiple requests in parallel. It focuses on the fast processing of LLMs with reduced latency and improved throughput and works well with complex models requiring heavy computational resources.

Key Features of vLLM:

Multi-GPU Efficiency: vLLM ensures the high performance of LLMs by optimizing multi-GPU setups for smooth parallel processing. This allows for a more effective distribution of model computations, minimizing bottlenecks and boosting overall throughput. vLLM’s native support for multi-GPU configurations ensures that workloads are efficiently managed, enhancing system performance.

Dynamic Continuous Batching: Rather than relying on fixed batch sizes, vLLM introduces continuous batching for the dynamic distribution of tasks. This approach improves resource management and efficiency in environments with variable workloads. With continuous management of input streams, idle time is reduced, especially for real-time applications that require constant data flow.

Speculative Decoding for Faster Inference: To minimize latency, vLLM uses speculative decoding to predict and validate future tokens in parallel. This preemptive approach helps speed up inference times, making it ideal for latency-sensitive applications like chatbots and real-time text generation. It’s one of the key innovations that sets vLLM apart in enhancing the responsiveness of large language models.

Optimized Memory Usage with Embedding Layers: vLLM uses optimized memory management to scale LLMs effectively. Its embedding layers are finely tuned for memory efficiency, ensuring that GPU memory is used in a balanced way. This optimization helps prevent memory overloads without compromising performance.

Flexibility with LLM Adapters: This tool supports LLM adapters, allowing developers to fine-tune models without entirely retraining them. This modular approach saves both time and resources, offering a more efficient path to customizing LLMs for specialized applications. So, developers get greater adaptability to work on specific use cases.

The use cases of vLLM include:

Hugging Face: Build and Manage Open-source LLMs

Hugging Face is a comprehensive platform for hosting, fine-tuning, and deploying open-source LLMs. It offers both cloud-based and on-premise solutions and provides access to a large range of models and tools. Backed by a large community-driven repository, it is widely used to build both small and large-scale AI applications.

Key Features of Hugging Face

Transformers Library: This flagship offering provides pre-trained models for natural language processing (NLP), computer vision, and audio tasks. It supports popular architectures like BERT, GPT, T5, and more, making it easy to fine-tune and deploy models for specific use cases.

Model Hub: It is a vast repository of pre-trained models contributed by the community and Hugging Face. With thousands of models available, users can find and use models for tasks like text classification, translation, summarization, and image generation.

Datasets Library: It provides access to a wide range of datasets for training and evaluation. It includes tools for data preprocessing, splitting, and streaming, making it easier to work with large datasets efficiently.

Integration with Popular Frameworks: Hugging Face integrates seamlessly with frameworks like PyTorch, TensorFlow, and JAX, allowing users to work within their preferred ecosystem.

Inference API: With a hosted inference API, Hugging Face allows developers to run open-source LLMs in production without the need for a complex infrastructure setup. It supports scalable, low-latency inference, making it ideal for real-time applications.

Hugging Face can be used to build applications like:

With its pre-trained models, user-friendly tools, and vibrant community, it facilitates innovation in AI

OLLMA vs vLLM vs Hugging Face

While all three popular tools- Ollama, vLLM, and Hugging Face, come with their unique features to simplify hosting and running open-source LLMs locally, let’s have a quick comparative analysis to understand them better.

Please refer to the below table that compares these tools on different factors like ease of use, performance, scalability, integration & flexibility, model support, deployment, and use case.

Table showing the comparison between Ollama, vLLM, Hugging Face

Choosing between these tools depends largely on your specific use case:

Leverage the Benefits of Open-Source LLMs for Your AI Solutions

Hosting and running open-source models doesn’t have to be a challenge. We’ve explored how effective LLMOps, using tools like Ollama, vLLM, and Hugging Face, can help streamline the process. Selecting the right tools based on your project’s unique requirements is crucial. It can provide significant benefits like cost savings, flexibility, data privacy, security, and, most importantly, the ability to customize AI solutions tailored to your specific business needs.

Depending on your use case, you can choose to work exclusively with open-source LLMs or adopt a hybrid approach by combining proprietary and open-source models within your AI systems.

Whether you’re looking to build AI solutions from scratch or need support optimizing existing applications, our comprehensive AI services are here to help. Also, be sure to check out our success stories to see how we’ve helped clients achieve real-world results with custom-built AI solutions.