My take
Why I use Llama
When data can't leave your infra or unit economics demand it, Llama is the answer. The 70B and 405B models are good enough for many production tasks, and fine-tuning closes the gap for narrow domains.
Want the broader stack philosophy? Read about how Sri picks tools or browse engineering insights.
Honest assessment
Strengths & tradeoffs
No tool is perfect. Here's what shines and what to watch for.
Strengths
- Open weights - true ownership and customization
- Wide size range from 1B to 405B+ parameters
- Mature fine-tuning and quantization ecosystem
- No per-token API costs
- Strong community of derivatives
Tradeoffs (honestly)
- Frontier capability still trails closed models
- Hosting cost and ops complexity is real
- License has commercial-use caveats above thresholds
Fit assessment
When to reach for Llama
Pick the right tool for the job.
Best fits
On-prem and air-gapped deployments
Fine-tuned models for narrow domains
High-volume batch inference
Local dev with Ollama
Not ideal for
Tasks needing absolute frontier capability
Teams without ML infra to operate models
Common use cases
Resources
Learn more
Curated official docs, tutorials, and writing on Llama.
Services
Where I apply Llama
Engagements where this technology shows up regularly.
Stack
Pairs well with Llama
Tools and platforms I commonly combine with this one.
AI & ML
More in this category
Model providers, frameworks, and stores that power my AI work.
Need help with Llama?
Whether you're starting fresh or optimizing an existing implementation, I can help you get the most out of this technology. Read more in insights or get in touch.