Llama-3.1-405B-Instruct-FP8-KV
| Property | Value |
|---|---|
| Parameter Count | 406B parameters |
| Model Type | Instruction-tuned LLM |
| License | llama3.1 |
| Tensor Type | BF16, F8_E4M3 |
What is Llama-3.1-405B-Instruct-FP8-KV?
This is an optimized version of Meta's Llama 3.1 405B model, specifically quantized using AMD's Quark technology. The model maintains impressive performance while achieving significant efficiency improvements through FP8 quantization, including specialized KV cache optimization.
Implementation Details
The model employs a sophisticated quantization strategy that targets all linear layers except the "lm_head". It uses FP8 symmetric per-tensor quantization for weights, activations, and KV cache, achieving a balance between model size and performance.
- Comprehensive FP8 quantization across model components
- Specialized KV cache optimization
- Maintains near-original model performance (Perplexity on wikitext2: 1.8951 vs original 1.8561)
- Compatible with vLLM backend for efficient deployment
Core Capabilities
- Efficient large-scale language processing
- Reduced memory footprint while maintaining performance
- Optimized for deployment in production environments
- Support for multi-GPU implementation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its implementation of FP8 quantization across weights, activations, and KV cache while maintaining performance very close to the original 405B parameter model. It's specifically optimized for deployment using the vLLM backend.
Q: What are the recommended use cases?
The model is ideal for production environments where efficient resource utilization is crucial while maintaining high-quality language model capabilities. It's particularly suitable for deployment scenarios requiring balanced performance and resource optimization.
