Open-Source locally run LLMs

Extracted Attributes

• Award

  Best New Tech of 2023 (All-In Podcast Bestie Awards)

• Model Scale

  1 billion to 2 trillion parameters

• Deployment Tools

  Ollama, OpenLLM, Hugging Face TGI, Ray Serve, vLLM

• Key Model Family

  Llama (Large Language Model Meta AI)

• Primary Benefits

  Data privacy, security, reduced latency, and cost-effectiveness

• Supported Platforms

  macOS, Windows, Linux

Timeline

• Meta AI releases the first version of Llama models, initially restricted to researchers. (Source: Wikipedia: Llama (language model))

  2023-02-01

• Open-source locally run LLMs are named 'Best New Tech' of the year at the fourth annual Bestie Awards. (Source: Document 47c5a1f9-3bf9-4d68-ae85-a92717b28f78)

  2023-12-01

• Llama 3 is released and integrated into virtual assistant features for Facebook and WhatsApp. (Source: Wikipedia: Llama (language model))

  2024-04-01

• Meta AI releases Llama 4, the latest iteration of the open-weights model family. (Source: Wikipedia: Llama (language model))

  2025-04-01

Llama (language model)

Llama ("Large Language Model Meta AI" serving as a backronym) is a family of large language models (LLMs) released by Meta AI starting in February 2023. Llama models come in different sizes, ranging from 1 billion to 2 trillion parameters. Initially only a foundation model, starting with Llama 2, Meta AI released instruction fine-tuned versions alongside foundation models. Model weights for the first version of Llama were only available to researchers on a case-by-case basis, under a non-commercial license. Unauthorized copies of the first model were shared via BitTorrent. Subsequent versions of Llama were made accessible outside academia and released under licenses that permitted some commercial use. Alongside the release of Llama 3 and a standalone website, Meta added virtual assistant features to Facebook and WhatsApp in select regions; both services used a Llama 3 model. However, the latest version is Llama 4, released in April 2025.

Web Search Results

• Guide to Local LLMs - Scrapfly

  Running open-source LLMs locally can be a rewarding experience, but it does come with some hardware and software requirements. Here are the key components you'll need: [...] In this blog post, we'll explore what Local LLMs are, the best options available, their requirements, and how they integrate with modern tools like LangChain for advanced applications. ## Key Takeaways Learn what is a local llm and how to deploy LLaMA, Qwen, and Mistral models locally with GPU acceleration and LangChain integration. [...] ## What is a Local LLM? A Local LLM is a machine learning model deployed and executed on local hardware, rather than relying on external cloud services. Unlike cloud-based LLMs, Local LLMs enable organizations to process sensitive data securely while reducing reliance on external servers. These models offer greater privacy, reduced latency, and enhanced control over customizations, making them ideal for use cases requiring high levels of confidentiality and adaptability.

• How to Run a Local LLM: Complete Guide to Setup & Best Models ...

  For those looking for a straightforward way to get started with running LLMs locally, Ollama offers a user-friendly experience. After installing Ollama (available for macOS, Windows, and Linux), you can easily download and run a wide variety of open-source models directly from the command line. For instance, to use a model like Deepseek R1, you would typically open your terminal and use a simple command such as `ollama pull deepseek-r1:14b` to download the 14b parameter varian. Once downloaded, [...] By running an LLM locally, you have the freedom to experiment, customize, and fine-tune the model to your specific needs without external dependencies. You can choose from a wide range of open-source models, tailor them to your specific tasks, and even experiment with different configurations to optimize performance. [...] If so, running Large Language Models (LLMs) locally could be the answer you've been looking for. Local LLMs offer a cost-effective and secure alternative to cloud-based options. By running models on your own hardware, you can avoid the recurring costs of API calls and keep your sensitive data within your own infrastructure. This is particularly beneficial in industries like healthcare, finance, and legal, where data privacy is paramount.

• Self-Hosted LLM: A 5-Step Deployment Guide

  What are some popular open-source tools for self-hosting LLMs, and what are their key features? OpenLLM with Yatai is a good option for deploying and managing LLMs, offering features like serving models via API, LangChain integration, and quantization support. Ray Serve on a Ray cluster provides a scalable model-serving library, framework-agnostic compatibility, and robust monitoring capabilities. Hugging Face TGI is a streamlined solution for running pre-trained Hugging Face models.

• The Best Open-Source LLMs in 2026 - BentoML

  Data security. Open-source LLMs can be run locally, or within a private cloud infrastructure, giving users more control over data security. By contrast, proprietary LLMs require you to send data to the provider’s servers, which can raise privacy concerns. [...] Generally speaking, open-source LLMs are models whose architecture, code, and weights are publicly released so anyone can download them, run them locally, fine-tune them, and deploy them in their own infrastructure. They give teams full control over inference, customization, data privacy, and long-term costs. However, the term “open-source LLM” is often used loosely. Many models are openly available, but their licensing falls under open weights, not traditional open source. [...] How can I optimize LLM inference performance?# One of the biggest benefits of self-hosting open-source LLMs is the flexibility to apply inference optimization for your specific use case. Frameworks like vLLM and SGLang already provide built-in support for inference techniques such as continuous batching and speculative decoding.

• The 11 best open-source LLMs for 2025 - n8n Blog

  There are several ways to run LLMs locally. The easiest approach is to use one of the available frameworks, which can get you up and running in just a few clicks: [...] 💡 For a detailed guide on these frameworks and how to use them, check out a comprehensive guide on running local LLMs. ## Wrap Up In this article, we've highlighted that the best open-source LLM depends on your specific use case, as models like Llama3, Mistral, and Falcon 3 excel in different areas such as speed, accuracy, or resource efficiency. We emphasized evaluating models based on factors like task requirements, deployment setup, and available resources. [...] There are at least 3 easy ways to build projects with open-source LLMs with n8n LangChain nodes: 1. Run small Hugging Face models with a User Access Token completely for free. 2. If you want to run larger models or need a quick response, try the Hugging Face service called Custom Inference Endpoints. 3. If you have enough computing resources, run the model via Ollama locally or self-hosted.