NVIDIA NIM microservices improve LLM inference efficiency at scale.

According to the NVIDIA Technology Blog, as large-scale language models (LLMs) continue to evolve at an unprecedented pace, enterprises are increasingly focused on building generative AI-based applications that maximize throughput and minimize latency. These optimizations are essential to lower operational costs and deliver superior user experiences.

Key metrics for measuring cost effectiveness

When a user sends a request to LLM, the system processes the request and generates a response by outputting a series of tokens. To minimize latency, multiple requests are often processed simultaneously. Throughput It measures the number of successful operations per unit of time, such as tokens per second, which is important for determining how well a business can handle concurrent user requests.

HiddenTime to First Token (TTFT) and Inter-Token Latency (ITL) are measured as delays before or between data transmissions. Lower latency ensures smooth user experiences and efficient system performance. TTFT measures the time it takes for a model to generate the first token after receiving a request, while ITL measures the interval between successive tokens.

Balancing throughput and latency

Enterprises need to balance throughput and latency based on the number of concurrent requests and the delay budget, which is the amount of delay that end users can tolerate. Increasing the number of concurrent requests can improve throughput, but it can also increase the latency of individual requests. Conversely, maintaining a set delay budget can optimize the number of concurrent requests to maximize throughput.

As the number of concurrent requests increases, businesses can deploy more GPUs to maintain throughput and user experience. For example, a chatbot that handles a surge in shopping requests during peak times will need multiple GPUs to maintain optimal performance.

How NVIDIA NIM Optimizes Throughput and Latency

NVIDIA NIM microservices provide a solution that maintains high throughput and low latency. NIM optimizes performance through techniques such as runtime refinement, intelligent model representation, and custom throughput and latency profiles. NVIDIA TensorRT-LLM further improves model performance by tuning parameters such as the number of GPUs and batch size.

Part of the NVIDIA AI Enterprise family, NIM is extensively tuned to ensure high performance for each model. Technologies such as Tensor Parallelism and in-flight batching process multiple requests in parallel to maximize GPU utilization, increase throughput, and reduce latency.

NVIDIA NIM Performance

Using NIM, enterprises have reported significant improvements in throughput and latency. For example, NVIDIA Llama 3.1 8B Instruct NIM delivers 2.5x faster throughput, 4x faster TTFT, and 2.2x faster ITL compared to the best open source alternative. A live demo showed that NIM On produced output 2.4x faster than NIM Off, demonstrating the efficiency gains that NIM’s optimized technology delivers.

NVIDIA NIM sets a new standard for enterprise AI, delivering unmatched performance, ease of use, and cost efficiency. Businesses that improve customer service, streamline operations, and drive innovation within their industries can benefit from NIM’s robust, scalable, and secure solutions.

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