Are you looking to enhance the performance of large language models using NVIDIA’s cutting-edge technology? Well, you’re in luck. Meta’s Llama 3.1 405B model has seen a significant boost in performance thanks to NVIDIA’s TensorRT Model Optimizer. This collaboration has resulted in up to a 1.44x increase in throughput when running on H200 GPUs.
#### Elevating Llama 3.1 405B Inference Throughput with TensorRT-LLM
The TensorRT-LLM integration has already showcased impressive inference throughput for the Llama 3.1 405B model, achieved through various optimizations such as in-flight batching, efficient KV caching, and streamlined attention kernels. These advancements have not only accelerated inference performance but also maintained lower precision compute, ensuring optimal efficiency.
With the addition of support for the official Llama FP8 quantization recipe, which calculates scaling factors to preserve maximum accuracy, TensorRT-LLM has raised the bar for performance standards. User-defined kernels like matrix multiplications from FBGEMM are also optimized through plug-ins inserted into the network graph during compilation, further enhancing efficiency.
#### Unleashing Maximum Performance with TensorRT Model Optimizer
NVIDIA’s custom FP8 post-training quantization recipe, available through the TensorRT Model Optimizer library, has played a crucial role in enhancing the throughput of Llama 3.1 405B while reducing latency. This recipe, which integrates FP8 KV cache quantization and self-attention static quantization, effectively minimizes inference compute overhead.
Table 1 showcases the remarkable improvements in maximum throughput performance across different input and output sequence lengths on an 8-GPU HGX H200 system. With eight NVIDIA H200 Tensor Core GPUs featuring high-speed HBM3e memory and NVLink Switches, the system delivers impressive GPU-to-GPU bandwidth of 900 GB/s.
| Input | Output Sequence Lengths | TensorRT Model Optimizer FP8 | Official Llama FP8 Recipe | Speedup |
|——|———–|————–|—————-|———-|
| 2,048 | 128 | 463.1 | 399.9 | 1.16x |
| 32,768 | 2,048 | 320.1 | 230.8 | 1.39x |
| 120,000 | 2,048 | 71.5 | 49.6 | 1.44x |
Furthermore, Table 2 illustrates the minimum latency performance using the same sequence lengths, highlighting the speedup achieved with the TensorRT Model Optimizer.
| Input | Output Sequence Lengths | TensorRT Model Optimizer FP8 | Official Llama FP8 Recipe | Speedup |
|——|———–|————–|—————-|———-|
| 2,048 | 128 | 49.6 | 37.4 | 1.33x |
| 32,768 | 2,048 | 44.2 | 33.1 | 1.33x |
| 120,000 | 2,048 | 27.2 | 22.8 | 1.19x |
These results underscore the superior performance delivered by H200 GPUs with TensorRT-LLM and TensorRT Model Optimizer, both in latency-optimized and throughput-optimized scenarios. The TensorRT Model Optimizer FP8 recipe has also demonstrated comparable accuracy with the official Llama 3.1 FP8 recipe on benchmark tests, showcasing the efficiency of the optimization techniques.
#### Achieving Efficiency with INT4 AWQ on Two H200 GPUs
For developers facing hardware constraints, the INT4 AWQ technique in the TensorRT Model Optimizer presents a game-changer. This method compresses the Llama 3.1 405B model, enabling it to fit on just two H200 GPUs by significantly reducing the memory footprint. By compressing weights to 4-bit integers and encoding activations using FP16, developers can achieve optimal performance with minimal hardware resources.
Tables 4 and 5 further demonstrate the maximum throughput and minimum latency performance measurements, validating the efficiency and accuracy of the INT4 AWQ approach compared to Meta’s official FP8 recipe.
| Input | Output Sequence Lengths | TensorRT Model Optimizer INT4 AWQ | Speedup |
|——|———–|————–|———-|
| 2,048 | 128 | 75.6 | 21.6 |
| 32,768 | 2,048 | 28.7 | 18.7 |
| 60,000 | 2,048 | 16.2 | 12.8 |
NVIDIA’s advancements in the TensorRT Model Optimizer and TensorRT-LLM technology are revolutionizing the landscape for running large language models like Llama 3.1 405B. These enhancements not only offer developers enhanced performance and efficiency but also provide flexibility and cost-efficiency, catering to a wide range of hardware environments and requirements.
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