Meta's efficient Llama 4 model with an industry-leading 10M token context window. 109B total parameters (17B active per expert, 16 experts). Natively multimodal and open source.
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Key Features
109B total parameters (17B active per expert, 16 experts)
Industry-leading 10M token context window
Mixture-of-Experts (MoE) architecture
Natively multimodal (text + image)
Open source (Llama license)
Fits on a single H100 server node
Common Use Cases
Ultra-long-context applications
Entire codebase analysis
Large-scale document processing
Open-source deployments
Research on long-context understanding
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Frequently Asked Questions
What is Meta's latest generation of open-source LLMs in late 2024?
Meta's latest generation is the Llama 3.1 series, which includes models of various sizes such as Llama 3.1 8B, Llama 3.1 70B, and the very large Llama 3.1 405B. These models are released with open weights and are known for their strong performance, particularly in reasoning, coding, and instruction following, with context windows typically around 128K tokens.
Are Meta's Llama 3.1 models free for commercial use?
Llama 3.1 models are generally released under a permissive license that allows for free research and commercial use. However, for very large-scale commercial deployment by major cloud providers or large enterprises, specific terms or acceptable use policies might apply. Users are responsible for their own hosting and inference costs if self-hosting.
What are the common ways to access and use Meta's Llama 3.1 models?
Llama 3.1 models can be accessed in several ways: by downloading the open weights and self-hosting, through various cloud platforms and API providers that offer managed access to Llama models (e.g., Hugging Face, Perplexity, Fireworks AI, AWS, Google Cloud, Azure), or via Meta's own research initiatives and tools.
How do Llama 3.1 models compare to closed-source models from OpenAI or Anthropic?
The largest Llama 3.1 models, like the 405B version, aim to be competitive with leading closed-source models in terms of performance on various benchmarks, especially in reasoning, coding, and instruction following. While they offer the advantage of open access and customizability, they may sometimes lag slightly behind the absolute cutting-edge capabilities or specific features (like advanced multimodality or tool use) of the very latest proprietary models.