Mistral Large review: the best European-hosted AI for data residency

Llama 3 requires significant hardware (16GB+ GPU). For most use cases, the API is cheaper. For high-volume or privacy-sensitive applications, self-hosting makes sense. I self-host for client work with sensitive data. For everything else, the API is the better value.

For 70% of use cases: yes, especially for chat, summarization, and content generation. For 30%: no, complex reasoning, coding, anything requiring the best model. I use Llama 3 for high-volume tasks and GPT-4 for complex reasoning. The combination is the most cost-effective.

Fine-tuning Llama 3 on 100K examples costs about $200-$500 in cloud GPU time. For specific use cases (customer support, domain-specific Q&A), the investment is worth it. For general use, the base model is good enough.

Llama 3 is the most popular but Mistral is faster and Qwen is better for Chinese. The choice depends on your use case. For English, Llama 3 or Mistral. For Chinese, Qwen. For code, CodeLlama. I use Llama 3 for English and Qwen for Chinese.