Open Source LLMs: Best Models for Running Locally

Have you ever wanted to chat with an AI companion that never phones home, never hits a paywall, and runs completely on your own hardware? That's the promise of open source LLMs local — models you can download, run, and customize on your own PC. Whether you're building a personal assistant, a creative writing partner, or just curious about the technology, the ability to deploy these powerful language models locally is transforming what's possible. In this guide, we'll explore the best open source LLMs for local deployment, from the massive LLaMA 3 to the efficient Mistral family, and show you how to get started running AI on your PC today.

Running an LLM on your own machine isn't just about privacy — it's about control. You can fine-tune the model on your own data, create role-playing characters, or build a chatbot that's completely offline. But with so many options, it's easy to get overwhelmed. We'll break down the top models by use case, hardware requirements, and community support.

Why Run LLMs Locally?

Before diving into specific models, let's talk about the why. When you use a cloud-based AI service, your conversations are sent to a remote server. With local LLM deployment, everything stays on your machine. No data leaves your computer. That's a huge advantage for privacy-sensitive applications, like personal journaling, brainstorming, or even NSFW character interaction (where discretion is often preferred). Plus, there's no subscription fee — you just need the hardware.

Another benefit is customization. Many open source models can be fine-tuned using tools like LoRA (Low-Rank Adaptation) or full fine-tuning, allowing you to create a chatbot that knows your favorite books, your sense of humor, or your preferred style of conversation. And because you control the inference, you can adjust parameters like temperature and top-p to get exactly the response style you want.

Top Open Source LLMs for Local Use

Here are the most popular models you can run on consumer hardware, from lightweight to heavy.

LLaMA 3 Local (8B and 70B)

Meta's LLaMA 3 family is one of the most capable open source LLM families. The 8B parameter model is a sweet spot for most users: it can run on a decent consumer GPU (like an RTX 3060 with 12GB VRAM) using 4-bit quantization. The 70B model requires a lot more firepower (ideally multi-GPU or at least 48GB VRAM), but it approaches GPT-4 level performance in benchmarks. For LLaMA 3 local deployment, the 8B version is recommended for most home setups.

Key strengths: Strong reasoning, good instruction following, large context window (up to 8K tokens). It's excellent for general chat, writing assistance, and even basic coding.

Tip: If you have 8GB VRAM, use a quantized version like LLaMA 3 8B Q4_K_M (4-bit). It runs smoother and uses less memory.

Mistral Local (7B and Mixtral 8x7B)

Mistral AI has become a darling of the open source community. Mistral 7B punches above its weight, often beating larger models in benchmarks. It's efficient enough to run on a laptop GPU (like an RTX 2050) with quantization. For those with more VRAM, Mixtral 8x7B is a mixture-of-experts model that delivers excellent performance at around 45GB of memory when quantized.

Key strengths: Very fast inference, low memory footprint, strong in multilingual tasks (especially French). Great for real-time chat applications.

Phi-3 from Microsoft

Phi-3 is a small but mighty model family. The 3.8B parameter version can run on a CPU with minimal RAM (like 4GB system RAM). It's designed for edge devices and is surprisingly capable for its size. If you're running on an older PC or want a model that can do quick tasks without a GPU, Phi-3 is a strong choice.

Hardware Requirements for Running AI on PC

To run these models locally, you need to understand your hardware. The two main factors are RAM (system memory for CPU inference) and VRAM (GPU memory for GPU inference).

• CPU-only: You can run smaller models like Phi-3 or Mistral 7B quantized to 4-bit on a modern CPU with 8-16GB RAM. Expect 5-20 tokens per second.
• GPU (8GB VRAM): LLaMA 3 8B Q4 or Mistral 7B Q4 fits comfortably. You'll get 20-40 tokens per second.
• GPU (16GB+ VRAM): You can run larger quantized models like LLaMA 3 70B Q4 (requires ~48GB, so you'd need two 24GB GPUs). But 12-16GB is enough for 13B models like CodeLlama.

If you're just starting, a single RTX 3060 12GB is the best bang for your buck. It can handle most 7B-13B models comfortably at 4-bit quantization.

How to Set Up Local LLM Deployment

Getting a model running is easier than ever thanks to tools like Ollama and LM Studio. Here's a quick walkthrough using Ollama (available for Windows, macOS, Linux).

1. Install Ollama from ollama.ai.
2. Open a terminal and run: ollama run llama3
3. The tool will download the LLaMA 3 8B model automatically and start an interactive chat.

That's it. You can also run Mistral: ollama run mistral. For more control, use LM Studio, which offers a GUI to download models and adjust parameters like temperature.

Fine-Tuning for Custom Characters

One of the most exciting uses of open source LLMs is creating custom characters. With tools like Axolotl or Unsloth, you can fine-tune a base model on a dataset of conversations (e.g., a role-playing script) to give it a specific personality. For example, you could fine-tune Mistral 7B to act like a medieval wizard or a noir detective. This is where platforms like VirtFlirt shine — they provide a curated experience, but running your own model locally gives you limitless customization.

Sample dialogue from a fine-tuned wizard character:
"Ah, a traveler seeks wisdom! Very well. I shall speak of the ancient runes, but first — do you have any cheese? A wizard is always peckish."

Choosing the Right Model for Your Use Case

Not every model fits every task. Here's a quick guide:

• General chat / casual conversation: LLaMA 3 8B or Mistral 7B. They're well-rounded and handle small talk naturally.
• Creative writing / role-play: MythoMax (a fine-tune of LLaMA 2/3) or Nous-Hermes 2. These are trained on fiction and are more creative.
• Coding: CodeLlama 7B/13B or DeepSeek Coder. They excel at generating code snippets.
• Privacy-sensitive applications (NSFW): Many base models can be used, but be aware that some fine-tunes (like those for uncensored chat) are available. Always check the license.

Final Thoughts

The era of open source LLMs local is here, giving you the power to run advanced AI on your own terms. Whether you're experimenting with Mistral local, diving into LLaMA 3 local deployments, or just curious about running AI on PC, the tools are accessible and the community is thriving. For those who want to skip the setup and start chatting immediately with carefully crafted characters, check out VirtFlirt — a platform that combines the best open source models with a polished user experience, perfect for exploring AI companionship without the technical hassle.