Llama

Meta Llama is the king of Open Weights models. Llama 4 (2025) pushes 405B+ parameters, rivaling closed models like GPT-5.

When to Use

• Privacy: Run it on your own VPC (AWS Bedrock, Azure, or self-hosted).
• Fine-Tuning: It is the default base model for fine-tuning on domain data.
• Cost: Inference on Groq/Together AI is significantly cheaper than GPT.

Core Concepts

Models

• 405B: Frontier intelligence. Requires massive GPU clusters (or API).
• 70B: The workhorse. Smart enough for most tasks.
• 8B: Runs on a laptop (MacBook M3).

Quantization

Running models at 4-bit or 8-bit precision to fit in VRAM with minimal quality loss (GGUF, EXL2).

Llama Stack

Standardized tooling for building agentic apps on Llama.

Best Practices (2025)

Do:

• Use via API: Groq (LPU) runs Llama Instantaneously (>1000 tok/s).
• Fine-Tune 8B: For specific tasks (classification, SQL generation), a fine-tuned 8B beats a generic 70B.

Don't:

• Don't self-host 405B: Unless you have 8xH100s. Use an API provider.

References

• Llama Website