Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

Llama 3.3 70B

The Meta Llama 3.3 70B is a large language model engineered for text-based generative applications. It operates as a dense Transformer model, incorporating an optimized architectural design. This model variant is specifically instruction-tuned for dialogue, demonstrating proficiency in multilingual chat scenarios, code assistance, and synthetic data generation. Its development involved extensive pretraining on approximately 15 trillion tokens sourced from publicly available online datasets.

From an architectural perspective, Llama 3.3 70B integrates Grouped-Query Attention (GQA) to enhance inference scalability and efficiency. The model's training regimen includes supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), which are applied to align its outputs with human preferences for helpfulness and safety. A notable feature is its extended context window, supporting up to 130,000 tokens, enabling the processing and generation of longer text sequences for advanced use cases such as long-form summarization and complex multi-turn conversations.

The model is equipped with capabilities for multilingual inputs and outputs, encompassing languages such as English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Furthermore, it supports tool-use, providing developers with the ability to extend its functionality via custom function definitions and integration with third-party services. This design emphasizes efficiency and aims to reduce hardware requirements, thereby increasing the accessibility of high-quality AI for various applications.