Question 1

Is Llama truly free to use commercially?

Accepted Answer

Llama is free to download and use commercially for most developers and companies. However, platforms with over 700 million monthly active users require a separate commercial license directly from Meta. The license also includes an Acceptable Use Policy that restricts certain applications such as weapons development, CSAM, and some regulated activities.

Question 2

What is the difference between Llama 4 Scout and Maverick?

Accepted Answer

Scout (109B total, 17B active parameters) is optimized for massive context tasks with a 10 million-token window — ideal for large document analysis, entire codebase ingestion, and long-form summarization. Maverick (400B total, 17B active, 128 experts) is the higher-quality workhorse for coding assistants, chatbots, and complex reasoning, with a 1 million-token context. Both use Mixture-of-Experts architecture and are natively multimodal.

Question 3

How do I run Llama locally?

Accepted Answer

The most popular method is via Ollama (ollama.com), which lets you run Llama with a single terminal command. llama.cpp is another widely used option for CPU and quantized inference. For GPU-based serving, vLLM and HuggingFace Transformers are the standard choices. Model weights are available on Hugging Face (meta-llama organization) after accepting the license.

Question 4

How does Llama compare to GPT-4o and Claude on benchmarks?

Accepted Answer

Llama 4 Maverick scores 85.5 on MMLU and 86.4% on HumanEval, competitive with GPT-4o (87-88 MMLU range). On GPQA Diamond and advanced reasoning, proprietary models like Gemini 2.5 Pro and Claude 3.7 Sonnet currently lead, but Llama 4 competes strongly on cost-efficiency and open-weight flexibility. Llama 3.3 70B achieves 86.0% on MMLU, matching Amazon Nova Pro.

Question 5

Can I fine-tune Llama on my own data?

Accepted Answer

Yes. Fine-tuning is one of Llama's primary advantages over closed models. You can use standard tools like HuggingFace PEFT/LoRA, Unsloth, or Axolotl. Meta's Llama Stack framework provides standardized APIs for fine-tuning and deployment. Many cloud providers (AWS, Databricks, IBM watsonx) also offer managed fine-tuning services for Llama.

Question 6

What happened with Llama 4 and benchmark controversies?

Accepted Answer

Shortly after Llama 4's release in April 2025, some researchers reported that the version tested on benchmarks was a specially optimized variant not identical to the public weights. Artificial Analysis noted that scoring methodology significantly affected results — Scout's Intelligence Index changed from 36 to 43 depending on whether alternative-format answers were accepted. Meta did not formally address all discrepancies, though independent tests broadly confirmed competitive performance.

Question 7

Is Llama open source?

Accepted Answer

Technically, Llama is 'open-weight' rather than fully open-source by the OSI definition. The model weights are publicly downloadable and commercially usable, but Meta does not disclose full training data details, and the license restricts certain use cases. The Free Software Foundation classified Llama 3.1's license as nonfree in January 2025. The Open Source Initiative's definition also excludes Llama. Despite this, it is the most widely adopted openly-available large language model in the world.

Question 8

Which companies use Llama in production?

Accepted Answer

Major enterprises using Llama include Spotify (personalized recommendations and AI DJ), AT&T (customer service automation), Goldman Sachs (document review and code generation), DoorDash (operations), Block/Cash App (support systems), Nomura Holdings, Accenture (government chatbots), and IBM. The US government gained access to Llama models in November 2024 for public-sector applications.

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