Unleash Unprecedented Power with NVIDIA H100 GPUs and TensorRT-LLM for Mixtral 8x7B 🚀

Revolutionizing Language Models with NVIDIA H100 Tensor Core GPUs and TensorRT-LLM Software 🚀

As large language models (LLMs) expand in size and complexity, the demand for efficient and cost-effective performance solutions grows. NVIDIA recently announced a groundbreaking achievement with its H100 Tensor Core GPUs and TensorRT-LLM software, setting new performance records on the industry-standard MLPerf Inference v4.0 benchmarks. This showcases the outstanding capabilities of NVIDIA’s full-stack inference platform.

Mixtral 8x7B Model and Mixture-of-Experts Architecture

Developed by Mistral AI, the Mixtral 8x7B model utilizes a Mixture-of-Experts (MoE) architecture. This innovative design offers unique advantages in model capacity, training cost, and first-token serving latency when compared to traditional dense architectures. The performance of the Mixtral 8x7B model with NVIDIA’s H100 Tensor Core GPUs and TensorRT-LLM software has been exceptional.

Enhancing Throughput and Latency Optimization

In large-scale LLM deployments, optimizing query response times and throughput is crucial.
TensorRT-LLM supports in-flight batching to improve performance during LLM serving by replacing completed requests with new ones.
Choosing the right response time budget involves balancing throughput and user interactivity, with throughput versus latency plots serving as valuable tools.

Performance Gains with FP8 Precision

The NVIDIA Hopper architecture includes fourth-generation Tensor Cores supporting the FP8 data type, offering a peak computational rate twice that of FP16 or BF16.
TensorRT-LLM’s FP8 quantization enables the conversion of model weights into FP8 and the use of highly-tuned FP8 kernels for significant performance benefits.
The H100 GPU delivers nearly 50% more throughput within a 0.5-second response time limit.

Efficiency in Streaming Mode and Token Processing

While operating in streaming mode, the H100 GPUs and TensorRT-LLM exhibit impressive performance.
Results are reported as soon as an output token is generated, enabling high throughput even with minimal average time per output token.
Using FP8 precision, a pair of H100 GPUs can achieve a throughput of 38.4 requests per second with a mean time per output token of just 0.016 seconds.

High Throughput in Latency-Unconstrained Scenarios

In latency-unconstrained situations like offline tasks, such as data labeling and sentiment analysis, the H100 GPUs demonstrate remarkable throughput.
With FP8 precision, the H100 GPUs can process nearly 21,000 tokens per second at a batch size of 1,024.
The FP8 throughput capabilities of the Hopper architecture enable efficient processing of larger batches.

TensorRT-LLM: Optimization and Open-Source Support

TensorRT-LLM is an open-source library designed to optimize LLM inference, providing performance enhancements for popular LLMs through a simple Python API.
The library includes general LLM optimizations such as attention kernels, KV caching, and quantization techniques like FP8 or INT4 AWQ.
Mixtral with TensorRT-LLM can be integrated with NVIDIA Triton Inference Server software for seamless hosting.

Upcoming Innovations from NVIDIA

NVIDIA’s commitment to innovation extends to upcoming products based on the cutting-edge Blackwell architecture.
The anticipated GB200 NVL72, featuring 36 NVIDIA Grace CPUs and 72 NVIDIA Blackwell GPUs, aims to deliver significant speedups for real-time 1.8 trillion parameter MoE LLM inference.

Hot Take: Embracing the Future of Language Models with NVIDIA’s Revolutionary Technology 🌟

By leveraging the power of NVIDIA’s H100 Tensor Core GPUs and TensorRT-LLM software, you can unlock unparalleled performance and efficiency in your language model deployments. Stay ahead of the curve with innovative technologies designed to optimize throughput, latency, and precision, paving the way for groundbreaking advancements in the field of artificial intelligence. Join NVIDIA on the cutting edge of LLM innovation and prepare for a future where language models redefine the boundaries of what’s possible.