Faster Mixtral inference with TensorRT-LLM and quantization

Dec 27, 2023 ·

Mixtral is a new hardware-agnostic deep learning inference platform developed by Baseten. It enables faster inference times with its optimized graph structure and data layout, while providing flexibility for deployment across different hardware architectures. To further improve model performance, Baseten has leveraged TensorRT Low Latency Mode (LLM) and quantization techniques.

TensorRT LLM is an advanced inference optimization technology introduced by NVIDIA to accelerate the performance of deep learning models on GPUs. It works by reducing memory footprint and optimizing computational graphs while preserving accuracy across different hardware platforms. Through this optimization, Mixtral can achieve comparable performance as compared to using native TensorFlow/Pytorch implementations on NVIDIA GPUs.

Quantization is another technique used by Baseten to optimize the efficiency of the Mixtral platform. This process reduces the size of models and makes them easier to deploy while still maintaining accuracy. Compared to non-quantized models, the quantized version of Mixtral can reduce the latency of inference by up to 90%, while also taking up 30% less space.

To summarize, Baseten has used TensorRT LLM and quantization techniques to optimize the performance of their Mixtral platform. These optimizations ensure that the platform is able to run efficiently across different hardware architectures, while still maintaining accuracy. In addition, the use of these techniques has helped to reduce the latency and size of the models, making them more efficient and easier to deploy.