Llama (Large Language Model Meta AI) is Meta's family of open-weight large language models, first released in February 2023. Unlike GPT-4 or Claude, Llama's weights are publicly downloadable — meaning anyone can run the model on their own hardware, fine-tune it on custom data, or build products without API fees. Llama 3.1 405B (released July 2024) became the first open-source model to match GPT-4 performance on major benchmarks.
Open-weight vs closed: what the difference actually means
| Property | Closed (GPT-4, Claude) | Open-weight (Llama, Mistral) |
|---|---|---|
| Weights accessible | No — API only | Yes — downloadable |
| Run locally | No | Yes (with enough VRAM) |
| Data privacy | Sent to vendor | Stays on your machine |
| Cost at scale | Per-token API fees | Hardware cost only |
| Fine-tuning | Limited (OpenAI fine-tune API) | Full control |
| Commercial use | Via API terms | Llama license (mostly free) |
Running Llama 3.1 8B locally requires approximately 16GB of GPU VRAM (e.g., an RTX 3090 or 4090). The 70B model requires ~140GB VRAM — typically a multi-GPU setup or cloud instance. Quantized versions (4-bit via GGUF) can run the 8B model on 6GB VRAM, making it accessible on consumer hardware.
The open-source model ecosystem
| Model | Creator | Params | Specialty |
|---|---|---|---|
| Llama 3.1 | Meta | 8B / 70B / 405B | General purpose, Apache-like license |
| Llama 3.3 | Meta | 70B | Instruction following, matches 405B |
| Mistral 7B | Mistral AI | 7B | Fast, efficient, Apache 2.0 |
| Mixtral 8x7B | Mistral AI | 46.7B MoE | Strong MoE, open weights |
| Qwen 2.5 | Alibaba | 0.5B–72B | Strong coding, multilingual |
| DeepSeek V3 | DeepSeek | 671B MoE | Matches GPT-4 at fraction of cost |
| Phi-3 | Microsoft | 3.8B | Tiny but capable, mobile-friendly |
| Gemma 2 | 2B / 9B / 27B | Efficient, open weights |
Running Llama 3 locally with Ollama (simplest method)
# Install: https://ollama.ai
# Then in terminal:
# ollama pull llama3.1
# ollama serve
import requests
response = requests.post(
"http://localhost:11434/api/generate",
json={
"model": "llama3.1",
"prompt": "Explain gradient descent in one paragraph.",
"stream": False
}
)
print(response.json()["response"])On LumiChats
LumiChats includes Llama 3.1, Mistral, and other open-source models in its model selection, letting you compare them directly against GPT-4o and Claude.
Try it free