NVIDIA NIM 1.4 Ready to Deploy with 2.4x Faster Inference

Unlocking Faster AI Inference: NVIDIA NIM 1.4

Summary

NVIDIA NIM 1.4 is set to revolutionize AI model inference with a significant boost in performance, offering up to 2.4 times faster inference out-of-the-box. This latest version incorporates substantial improvements in kernel efficiency, runtime heuristics, and memory allocation, making it a game-changer for businesses that rely on quick responses and high throughput for generative AI applications.

The Need for High-Performance Inference

The demand for ready-to-deploy high-performance inference is growing rapidly as generative AI reshapes industries. NVIDIA NIM addresses this need by providing production-ready microservice containers for AI model inference, continuously improving enterprise-grade generative AI performance.

Key Features of NVIDIA NIM 1.4

Enhanced Performance

NVIDIA NIM 1.4 includes significant improvements in kernel efficiency, runtime heuristics, and memory allocation, translating into up to 2.4 times faster inferencing compared to NIM 1.2. These advancements are crucial for businesses that rely on quick responses and high throughput for generative AI applications.

Full-Stack Accelerated Computing

NIM benefits from continuous updates to full-stack accelerated computing, which enhances performance and efficiency at every level of the computing stack. This includes support for the latest NVIDIA TensorRT and NVIDIA CUDA versions, further boosting inference performance.

Preconfigured Software

NIM brings together a full suite of preconfigured software to deliver high-performance AI inferencing with minimal setup, enabling developers to quickly get started with high-performance inference.

Continuous Innovation Loop

A continuous innovation loop means that every improvement in TensorRT-LLM, CUDA, and other core accelerated computing technologies immediately benefits NIM users. Updates are seamlessly integrated and delivered through updates to NIM microservice containers, eliminating the need for manual configuration and reducing the engineering overhead typically associated with maintaining high-performance inference solutions.

The Role of TensorRT-LLM

TensorRT-LLM is at the core of NIM, enabling it to achieve speed-of-light inference performance. With each release, NIM incorporates the latest advancements in kernel optimizations, memory management, and scheduling from TensorRT-LLM to improve performance.

Evaluating Inference Performance

Delivering world-class inference performance requires a full technology stack—chips, systems, and software—all contributing to boosting throughput, reducing energy consumption per token, and minimizing costs. MLPerf Inference is a key measure of inference performance, measuring inference throughput under standardized conditions with results subject to extensive peer review.

NVIDIA Full-Stack Solutions

NVIDIA offers full-stack solutions that optimize AI inference performance. These solutions include:

• NVIDIA TensorRT-LLM: Incorporates state-of-the-art features that accelerate inference performance for large language models (LLMs).
• NVIDIA Blackwell: Delivers up to 4 times more performance than the NVIDIA H100 Tensor Core GPU on the Llama 2 70B benchmark.
• NVIDIA H200 Tensor Core GPU: Achieves outstanding results on every benchmark in the data center category, including the newly added Mixtral 8x7B mixture-of-experts (MoE) LLM.

Table: Key Features of NVIDIA NIM 1.4

Table: NVIDIA Full-Stack Solutions

Conclusion

NVIDIA NIM 1.4 is a significant step forward in AI model inference, offering up to 2.4 times faster performance. With its preconfigured software and continuous innovation loop, NIM is poised to meet the growing demand for high-performance inference in generative AI applications. By leveraging the latest advancements in kernel optimizations, memory management, and scheduling, NIM users can achieve remarkable gains in speed, scalability, and cost-effectiveness.