Trail guide
The best open source LLMs — and how to pick one
Open source large language models let you download the weights, run them on your own hardware, and keep your data inside your own fence. This guide compares the open source LLMs the community actually rides every day — by parameter count, license, and the memory you need to run them — and links straight to free downloads on ModelCorral.
Open source LLM comparison
| Model | Params | License | Memory needed | Best for |
|---|---|---|---|---|
| meta-llama/Llama-3.1-8B-Instruct Llama 3.1 | 8B | Llama 3.1 Community | ~16 GB (fp16) / ~6 GB (4-bit) | General chat and assistants with broad tooling support |
| mistralai/Mistral-7B-Instruct-v0.3 Mistral | 7B | Apache 2.0 | ~15 GB (fp16) / ~5 GB (4-bit) | Permissive commercial use and fast inference |
| Qwen/Qwen2.5-7B-Instruct Qwen 2.5 | 7B | Apache 2.0 | ~16 GB (fp16) / ~6 GB (4-bit) | Multilingual work, coding, and long context |
| google/gemma-2-9b-it Gemma 2 | 9B | Gemma Terms of Use | ~18 GB (fp16) / ~7 GB (4-bit) | Quality-per-parameter on a single consumer GPU |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B DeepSeek R1 (distill) | 7B | MIT | ~16 GB (fp16) / ~6 GB (4-bit) | Step-by-step reasoning and math on modest hardware |
| microsoft/Phi-3.5-mini-instruct Phi 3.5 | 3.8B | MIT | ~8 GB (fp16) / ~3 GB (4-bit) | Laptops, edge devices, and cheap batch jobs |
Memory figures are approximate rules of thumb: roughly 2 GB per billion parameters at fp16, or about 0.7 GB per billion at 4-bit quantization, plus room for context.
How to choose
Start with the license
Apache 2.0 and MIT models carry the fewest strings. Community licenses are usually fine for products, but read the acceptable-use terms first.
Then match the hardware
A 7–9B model quantized to 4-bit runs on an 8 GB consumer GPU or an Apple Silicon laptop. Above 30B you are into multi-GPU or serious cloud territory.
Then test on your own data
Leaderboards rank general ability. Your task is not general. Run two or three candidates against real prompts before committing.
Download an open source LLM
Every model page on ModelCorral lists the files, license, and a copy-paste download command. Pull weights with the CLI:
huggingface-cli download mistralai/Mistral-7B-Instruct-v0.3 --local-dir ./mistral-7b
Questions from the trail
What is an open source LLM?
An open source LLM is a large language model whose trained weights are published for download, so you can run it on your own hardware instead of calling someone else's API. Licenses vary: Apache 2.0 and MIT are fully permissive, while community licenses such as Llama's add usage conditions you should read before shipping.
Are open source AI models free to download?
Yes. Every model listed here can be downloaded at no cost through ModelCorral. Your only costs are the hardware or cloud GPU time you use to run them, and compliance with each model's license.
Which open source LLM is best?
There is no single winner. Pick by constraint: Mistral 7B and Qwen 2.5 7B for permissive licensing, Llama 3.1 8B for ecosystem support, DeepSeek R1 distills for reasoning, and Phi 3.5 mini when memory is tight.
Can I use open source LLMs commercially?
Apache 2.0 and MIT models can be used commercially with attribution requirements only. Community-licensed models such as Llama and Gemma allow most commercial use but carry acceptable-use policies and, in some cases, scale thresholds. Always read the license on the model page.