Neural Magic Releases Fully Quantized FP8 Version of Meta’s Llama 3.1 405B Model
Practical Solutions and Value
Neural Magic recently achieved a breakthrough in AI model compression by introducing a fully quantized FP8 version of Meta’s Llama 3.1 405B model. This advancement allows the massive model to fit seamlessly on any 8xH100 or 8xA100 system without common out-of-memory (OOM) errors. The new model solves memory constraints and enhances inference speeds by over 2X, without the need for CPU offloading or distribution across multiple nodes.
Features
– Fully quantized FP8 version enables the model to fit seamlessly on hardware without memory constraints.
– Achieves over 2X improvement in inference speeds without requiring CPU offloading or distribution across multiple nodes.
– Provides two key versions of the model: Meta-Llama-3.1-405B-Instruct-FP8-dynamic and Meta-Llama-3.1-405B-Instruct-FP8.
Quantization and Optimization
The model achieves remarkable efficiency through weight and activation quantization to the FP8 data type, reducing disk size and GPU memory requirements. It involves symmetric per-channel quantization and dynamic activation quantization on a per-token basis, ensuring optimal performance.
Deployment and Evaluation
The quantized model can be efficiently deployed using the vLLM backend. It has been evaluated on several benchmarks, achieving high accuracy across various tasks and few-shot settings.
Conclusion
The fully quantized FP8 version of Meta’s Llama 3.1 405B model by Neural Magic effectively reduces memory requirements and enhances inference speeds, making powerful AI models more accessible and practical for various users.
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