Open source AI models like Llama 3.1, Mistral, and Qwen run on your own hardware with zero data collection. Llama 3.1 70B is competitive with GPT-4 on many benchmarks. Ollama makes running them as simple as one command. I run several of these daily and the quality keeps getting better.
Why Open Source Matters for Privacy
The short version: Open source models run on your own hardware. No cloud, no third party, full transparency into the code.
Open source AI models run on your own hardware. No cloud, no third party, no data collection. You download the model, run it locally, and your conversations never leave your machine. This is the strongest form of AI privacy available.
Open source also means transparency. You can inspect the code, understand how the model works, and verify there are no backdoors or data collection mechanisms. I trust open source more than any privacy policy because I can actually see what's happening.
Top Open Source Models
Llama 3.1 70B
Meta70B paramsGeneral purpose, strong coding, good reasoning
Llama 3.1 8B
Meta8B paramsFast, lightweight, good for basic tasks
Mistral 7B
Mistral AI7B paramsFast, efficient, good at instruction following
Mixtral 8x7B
Mistral AI46.7B (12.9B active) paramsMoE architecture, strong performance, fast inference
Qwen 2.5 72B
Alibaba72B paramsStrong multilingual, good at math and coding
DeepSeek V3
DeepSeek671B (37B active) paramsBest open-weight model for reasoning and coding
Phi-3 Mini
Microsoft3.8B paramsTiny but capable. Runs on phones and laptops.
Gemma 2 9B
Google9B paramsGood balance of size and quality. Strong at summarization.
How to Run Them
The easiest way to run open source models is with Ollama. It handles downloading, quantization, and inference automatically. I have it set up on my laptop and it just works.
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Run Llama 3.1 8B (fast, good for daily use)
ollama run llama3.1
# Run Mistral 7B (fast, Apache licensed)
ollama run mistral
# Run Qwen 2.5 (strong multilingual)
ollama run qwen2.5
# Run Phi-3 Mini (tiny, runs on anything)
ollama run phi3Quantization: Making Models Smaller
Full-precision models are huge. Quantization reduces model size by lowering numerical precision, from 16-bit to 4-bit for example. A 70B model that needs 140GB at full precision can run in about 40GB with 4-bit quantization. Ollama handles this automatically so you don't have to think about it.
Open Source vs Proprietary: Quality Gap
The short version: The gap has shrunk a lot. Llama 3.1 70B and DeepSeek V3 are competitive with GPT-4 on most everyday tasks.
The gap between open source and proprietary models has shrunk dramatically. Llama 3.1 70B is competitive with GPT-4 on many benchmarks. DeepSeek V3 outperforms GPT-4 on reasoning tasks. For most everyday use cases, open source models are good enough.
The main remaining gap is in very complex reasoning, creative writing nuance, and following extremely detailed instructions. If you need the absolute best quality, proprietary models still have an edge. For everything else, open source works fine. I use a mix of both depending on the task.
What I Actually Do
I use open source models via Ollama for everyday tasks and anything sensitive. For tasks that need GPT-4 or Claude quality, I switch to NanoGPT. Both options protect your privacy, just at different levels of model quality.