With version 3.3, Meta delivers a refined Instruct model of the popular Llama series – specifically trained for helpful, confident and conversational behavior. Based on Llama 3 (70B), the model combines state-of-the-art language understanding with stable multi-turn interaction, improved tool use and high controllability. Llama 3.3 Instruct is ideal for AI assistants, chatbots, decision support systems and other applications where the focus is on user-friendliness and response quality.
LLaMA 3.3 70B Instruct
Meta AI
December 6, 2024
Llama 3.3 Community License (commercial use with restrictions for very large companies, see license text)
Auto-regressive, transformer-based language model
70.6 billion
Transformer with Grouped Query Attention (GQA) for efficient scaling
SentencePiece, 128k context length, supports multiple languages
The first generation of Meta AI’s LLaMA models marked the entry into the open source world of large language models. With a focus on efficiency and scientific openness, LLaMA 1 laid the foundation for subsequent iterations.
With LLaMA 2, the open source concept was consistently continued. The models were not only made more powerful, but also commercially usable – an important step towards broad industrial application.
LLaMA 3 brought significant improvements in training, model architecture and handling of complex tasks. Thanks to a greatly expanded pre-training dataset, the third generation achieved significantly better results in benchmarks and generative text processing.
This interim version was primarily used to optimize stability, security and inference speed. LLaMA 3.1 benefited from user feedback and set new standards in terms of prompt robustness and multiturn capability.
The latest generation combines over 15 trillion tokens of training data with over 25 million fine-tuned examples – making it one of the most powerful open source models in the world. LLaMA 3.3 builds on the findings of all its predecessors and represents the current pinnacle of LLaMA development.
We would be happy to advise you individually on which AI model suits your requirements. Arrange a no-obligation initial consultation with our AI experts and exploit the full potential of AI for your project!
LLaMA 3.3 70B was trained on an exceptionally large database: Over 15 trillion tokens of publicly available texts and source codes form the foundation of the pre-training. The knowledge cut-off is December 2023, which means that the model has very up-to-date knowledge of the world. In addition, over 25 million synthetically generated example pairs and carefully curated Instruct data from publicly accessible sources were used for fine-tuning.
The model supports several languages, including English, German, French, Italian, Portuguese, Hindi, Spanish and Thai, which enables versatile use in international contexts.
Recommended hardware specifications for ‘meta-llama/Llama-3.3-70B-Instruct’ with batch size 16, context length 32,000 tokens, weights FP8:
Recommended GPU configuration (inference, min. 376 GB VRAM):
Is LLaMA 3.3 70B the right AI model for your individual application? We will be happy to advise you comprehensively and personally.
Very high multilingual performance (8+ official languages)
Very large context window (128k)
Top performance on code, math and reasoning benchmarks
Commercial, but relatively open license
Tool use support, advanced fine-tuning
Community-driven security safeguards (Llama Guard, Prompt Guard etc.)
Potentially very high hardware and memory requirements (can only be operated in full mode with industry-standard hardware)
License with restrictions for very large companies/platform providers
Like all LLMs: bias, hallucinations, can generate unsafe outputs; not to be used in safety-critical or highly regulated scenarios
Many languages beyond the “officially” supported ones work, but no guarantees (fine-tuning required!)
Answers outside “Knowledge Cut-Off” December 2023 may be inaccurate
Rely on LLaMA 3.3 70B if you are looking for an open, powerful language model with an enormous context window, high precision and versatile capabilities. Whether for enterprise applications, research or product development – our experts will support you with selection, deployment and hosting.
For the smooth operation of Llama 3.3 70B, a GPU with at least 96 GB vRAM is recommended – ideally an Nvidia H100, B100 or comparable high-end GPU. For more complex applications or longer contexts, additional memory may be required, especially for parallel processing or fine-tuning.
The Llama 3 models react more sensitively to quantization than many other language models, as they have a particularly high information density per parameter. Aggressive quantization can therefore lead to a noticeable loss of quality – especially for demanding tasks such as logical reasoning, precise answers or longer dialogues. Lightweight quantizations (e.g. 8-bit) remain practicable for many use cases, but should be specifically evaluated.
The hardware required depends heavily on the model size: Smaller variants (e.g. Llama 3 8B) can already be run on a modern CPU or mid-range GPU. For models with more than 8 billion parameters (such as Llama 3.3 70B), the use of powerful GPUs with corresponding vRAM is mandatory – ideally in a cloud or server environment that is optimized for AI inference.
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