Meta's Llama is the most popular open-weights model family, enabling self-hosting with no per-token fees. Llama 3.3 70B-Instruct (Dec 2024) matches GPT-4o on most benchmarks while being free to run. Models are available on HuggingFace for download. Llama uses a custom commercial license (Meta Llama Community License) allowing commercial use for apps with <700M monthly active users. Key use cases: privacy-critical apps, cost-sensitive high-volume, fine-tuning on proprietary data, air-gapped deployments.
Each stage in order — click any step to read what it does.
Llama 3 family by size and deployment target.
The trade-offs worth knowing before you build this.
For healthcare, legal, and government use cases where data cannot leave the network, Llama + vLLM on private infrastructure is the only viable option. No API keys, no data egress, no vendor dependency.
A Llama 3.1 8B fine-tuned on 2000 domain-specific examples often outperforms GPT-4o on that specific task while running 100x cheaper. For repetitive, well-defined tasks, invest in fine-tuning.
Groq LPU hardware runs Llama 3.3 70B at 350+ tokens/second vs ~60 tokens/s for GPT-4o. For latency-critical apps (real-time voice, streaming chat), Groq + Llama delivers the best perceived speed at a fraction of the cost.
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