Llama-3.3-70B-Instruct-FP4
| Property | Value |
|---|---|
| Model Size | 70B parameters |
| License | NVIDIA Open Model License |
| Quantization | FP4 |
| Context Length | 128K tokens |
| Hardware Support | NVIDIA Blackwell |
What is Llama-3.3-70B-Instruct-FP4?
The NVIDIA Llama-3.3-70B-Instruct-FP4 is a highly optimized version of Meta's Llama 3.3 70B Instruct model, featuring advanced FP4 quantization that dramatically reduces memory requirements while maintaining impressive performance. This model represents a significant advancement in efficient AI deployment, achieving a 3.3x reduction in disk size and GPU memory requirements.
Implementation Details
The model utilizes TensorRT-LLM for deployment and features quantized weights and activations specifically in the linear operators within transformer blocks. The quantization process reduces bits per parameter from 16 to 4, while maintaining robust performance across various benchmarks.
- Optimized with nvidia-modelopt v0.23.0
- Supports context lengths up to 128K tokens
- Calibrated using the CNN/DailyMail dataset
- Compatible with NVIDIA Blackwell architecture
Core Capabilities
- Benchmark Performance: MMLU (81.1%), GSM8K_COT (92.6%), ARC Challenge (93.3%), IFEVAL (92.0%)
- Efficient deployment through TensorRT-LLM API
- Supports both commercial and non-commercial applications
- Optimized for Linux operating systems
Frequently Asked Questions
Q: What makes this model unique?
The model's FP4 quantization technique achieves remarkable memory efficiency while maintaining over 92% of the original model's performance across key benchmarks. This makes it particularly valuable for deployment in resource-constrained environments.
Q: What are the recommended use cases?
The model is suitable for a wide range of natural language processing tasks, particularly in production environments where memory efficiency is crucial. Its commercial-use license and optimized architecture make it ideal for enterprise applications requiring high-performance language modeling.
