Boosting Performance of Llama 3.1 405B with NVIDIA’s TensorRT Model Optimizer on H200 GPUs
Meta’s Llama 3.1 405B large language model (LLM) is experiencing a significant performance boost with NVIDIA’s TensorRT Model Optimizer. According to the NVIDIA Technical Blog, the enhancements have led to up to a 1.44x increase in throughput when utilized on NVIDIA H200 GPUs.
Enhanced Inference Throughput with TensorRT-LLM
1. Achieving remarkable inference throughput for Llama 3.1 405B model.
- Accelerated performance through optimizations like in-flight batching and KV caching.
- Integration of the Llama FP8 quantization recipe for maximum accuracy.
Improving Performance with TensorRT Model Optimizer
1. NVIDIA’s custom FP8 post-training quantization recipe enhances throughput and reduces latency.
- Utilizes FP8 KV cache quantization and self-attention static quantization to optimize performance.
- Brings significant improvements in maximum throughput performance on H200 GPUs.
Impressive Efficiency with INT4 AWQ Compression
1. Utilizing the INT4 AWQ technique to compress Llama 3.1 405B model for optimal performance.
- Allows the model to operate efficiently on just two H200 GPUs.
- Significantly reduces memory footprint with 4-bit integer weight compression.
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