Top 7 Open Source AI Models You Can Download Today

The era of paying $20 a month for a chatbot is ending. I’ve spent the last six months watching the “moat” around Big Tech crumble. Companies like Google and OpenAI want you to believe that AI is too big, too heavy, and too expensive for you to own. They are wrong.

Right now, you can download the “brains” of these systems onto your own hard drive. You can run them without an internet connection. You can talk to them without a corporation logging your every thought. This isn’t just a hobby for nerds anymore. It’s a shift in power.

I’ve tested dozens of models. Most are junk. Some are just copies of copies. But seven of them stand out. These are the models that actually work. Here is the breakdown of the best open-source AI models you can grab right now.

1. Llama 3: The Heavyweight Champion

Meta changed the game when they released Llama. Before Llama, open-source AI was a joke. Now, it’s a threat. Llama 3 is the latest version, and it is a beast. It comes in two main sizes: 8B (8 billion parameters) and 70B (70 billion parameters).

I ran the 8B version on a standard MacBook M2. It was fast. It felt like talking to GPT-3.5, but it was living on my desk. The 70B version is the real star, though. It rivals GPT-4 in many benchmarks like MMLU (General knowledge) and GSM8K (Math).

Why it matters:

• The Ecosystem: Since everyone uses Llama, every new tool supports it first.
• Fine-tuning: You can use a technique called QLoRA to teach Llama 3 your specific data for less than $50.
• Context Window: It handles 8k tokens out of the box, which is enough for most emails and short documents.

The Catch: It isn’t “pure” open source. Meta’s license says you can’t use it to train other models, and if you have 700 million users, you have to ask for permission. For you and me? It’s free.

2. Mistral 7B: The Giant Killer

Mistral AI is a French company that decided efficiency was more important than size. Their Mistral 7B model is legendary in the AI community. It’s small enough to run on a phone, yet it beats Llama 2 13B in almost every test.

I use Mistral 7B when I need speed. If I’m summarizing 50 PDFs at once, I don’t want to wait for a 70B model to “think.” Mistral 7B flies. It uses “Sliding Window Attention” to handle longer sequences of text without slowing down.

Hardware Tip: You can run this on an 8GB VRAM GPU (like an RTX 3060) with room to spare. If you use quantization (compressing the model), it takes up about 5GB of space.

3. Mixtral 8x7B: The Power of Experts

Mistral didn’t stop at 7B. They released Mixtral 8x7B, which uses a “Mixture of Experts” (MoE) architecture. Think of it as a room full of eight specialized mini-brains. When you ask a question, only the two best “brains” for that topic wake up to answer.

This gives you the power of a 45B model with the speed of a 12B model. It’s brilliant. In my testing, Mixtral handles code better than almost anything else in the open-weight world. It’s my go-to for Python scripts and debugging.

Key Stats:

• Parameters: 46.7B total, but only uses 12.9B per token.
• Context: 32k tokens. You can feed it a whole chapter of a book.
• License: Apache 2.0. This is the “gold standard” of licenses. You can do whatever you want with it.

4. Stable Diffusion XL (SDXL): The King of Pixels

AI isn’t just about text. Stable Diffusion XL is the reason your Twitter feed is full of AI art. Unlike Midjourney, which lives on a Discord server, Stable Diffusion lives on your PC.

I’ve seen people use SDXL to create everything from architectural renders to hyper-realistic portraits. Because it’s open source, the community has built thousands of “LoRAs”—tiny add-on files that teach the model a specific style, like “90s Anime” or “Cyberpunk City.”

The Catch: You need a good graphics card. Don’t try to run this on an integrated Intel chip. You want at least 12GB of VRAM for a smooth experience. If you have an NVIDIA card, you can use TensorRT to double the generation speed.

5. Whisper v3: The Gold Standard for Audio

OpenAI actually open-sourced something, and it’s the best tool in its category. Whisper is a speech-to-text model. I use it to transcribe my interviews. It’s better than the paid services I used to use.

Whisper v3 can handle 58 different languages. It doesn’t care if you have a thick accent or if there is background noise. It just works. I’ve run the “Large” version of Whisper on a consumer laptop, and it transcribes audio faster than real-time. 30 minutes of audio takes about 2 minutes to process.

6. Gemma: Google’s Lightweight Contender

Google got tired of Meta getting all the glory. They released Gemma, which is built from the same tech as their Gemini models. Gemma comes in 2B and 7B sizes.

Is it better than Mistral? Not really. But it’s very “clean.” Google spent a lot of time on safety and formatting. If you are building an app that needs to run on a mobile device, Gemma 2B is a great choice. It’s tiny, but it’s smart enough to follow instructions perfectly.

7. DBRX: The Enterprise Powerhouse

Databricks spent $10 million to train DBRX. It’s a massive MoE model with 132 billion parameters. It’s designed to beat GPT-4, and in some coding tasks, it actually does.

This isn’t a model for your laptop. You need a server—or a very expensive workstation with multiple A100 GPUs. But for businesses that want to own their data and have GPT-4 level intelligence, DBRX is the current peak of open-source tech.

The Hardware Reality Check: Can You Actually Run These?

I get this question every day. “Do I need a $5,000 PC?” The answer is: maybe. It depends on quantization.

Raw AI models are “heavy.” They use 16-bit floats (FP16). A 7B model in FP16 takes about 14GB of VRAM. Most people don’t have that. But we can “quantize” the model down to 4-bit or 8-bit. This shrinks the model size by 70% with almost no loss in intelligence.

• 4GB VRAM: You can run Mistral 7B (4-bit) or Gemma 2B. It will be slow.
• 8GB VRAM: This is the sweet spot. You can run any 7B or 8B model comfortably.
• 24GB VRAM (RTX 3090/4090): Now you’re cooking. You can run 30B models or heavily compressed 70B models.
• Mac Studio (64GB+ RAM): Apple’s Unified Memory is a cheat code for AI. You can run the massive 70B Llama 3 without a sweat.

The Licensing Trap: Open Source vs. Open Weights

We need to be honest here. Most of these aren’t “Open Source” by the strict definition of the Open Source Initiative (OSI).

A truly open-source project gives you the code, the data, and the right to do anything. Models like Llama 3 are “Open Weights.” You get the finished brain, but Meta won’t tell you exactly what “books” they fed it to make it smart.

If you are a developer, stick to Apache 2.0 or MIT licenses (like Mistral or Falcon) if you want zero legal headaches. If you just want a personal assistant, the Llama license is fine.

How to Download and Run These Locally

Don’t bother with complex Python scripts if you’re just starting. Use these three tools. I’ve tested them all, and they make the process “one-click.”

1. LM Studio

This is the easiest way to start. It’s a GUI for Windows, Mac, and Linux. You search for a model (like Llama 3), click download, and start chatting. It handles all the technical “quantization” stuff in the background.

2. Ollama

If you like the command line, Ollama is king. It runs as a background service. You type ollama run llama3 and you’re talking to it in seconds. It’s very lightweight and great for developers who want to connect AI to their own apps via an API.

3. Pinokio

Stable Diffusion and Whisper can be a pain to install because of “dependencies” (other software they need to run). Pinokio is a browser that installs these complex AI tools with one click. It’s a lifesaver for non-technical users.

Topical Depth: The Role of RAG

Downloading a model is only half the battle. If you ask Llama 3 about a meeting you had yesterday, it won’t know the answer. It’s frozen in time.

To fix this, we use Retrieval-Augmented Generation (RAG). Instead of retraining the whole model (which is expensive), you give the model a “library” of your files. When you ask a question, the system looks through your files, finds the right page, and shows it to the AI.

This is why local AI is so powerful for privacy. You can index your entire medical history or company secrets, and the AI can analyze it without a single byte of data leaving your room.

The Future: Small Language Models (SLMs)

The trend for 2026 isn’t “bigger.” It’s “smarter and smaller.” We are seeing models like Microsoft’s Phi-3 that are tiny but perform like giants. The goal is to have an AI “agent” living on your smartwatch or your phone that doesn’t need the cloud.

I saw a demo recently of an SLM running on a Raspberry Pi. It wasn’t writing novels, but it was controlling a smart home perfectly. That’s where the real revolution happens—when the AI is invisible and local.

Final Verdict: Which One Should You Pick?

Stop overthinking it. Here is my advice:

• If you want the smartest possible chat: Download Llama 3 70B (if you have the RAM) or 8B (if you don’t).
• If you want to code: Use Mixtral 8x7B.
• If you want to transcribe audio: Use Whisper v3.
• If you want to make art: Use Stable Diffusion XL.

The “moat” is gone. The models are here. Go download one and see for yourself. You don’t need a subscription to be at the cutting edge of technology anymore. You just need a hard drive and a bit of curiosity.