The prerequisite for sizing and TCO estimation is benchmarking the performance of each deployment unit, e.g., an inference server. The goal of this step is to measure the throughput a system can produce under load, and at what latency. These throughput and latency metrics, together with quality of service requirements (e.g., max latency) and expected peak demand (e.g., max concurrent users or requests per second), will help estimate the required hardware, such as sizing the deployment. In turn, sizing information is a prerequisite for estimating the total cost of ownership (TCO) of the given s
LLM Inference Benchmarking: How Much Does Your LLM Inference Cost? | NVIDIA Technical BlogOnce raw benchmark data are collected, they are analyzed to gain insight into the various performance characteristics of the system. Read our LLM inference benchmarking guide, where we gather NIM performance data with GenAI-perf and use a simple Python script to analyze the data. For example, performance data provided by GenAI-perf can be used to establish the latency-throughput trade-off curve, shown in Figure 1. Each dot on this graph corresponds to a “concurrency” level, that is, the number of concurrent requests being put into the system at any given time throughout the benchmark process
LLM Inference Benchmarking: How Much Does Your LLM Inference Cost? | NVIDIA Technical Blog…Based on the NVIDIA Blackwell Architecture, this compact, energy-efficient platform delivers powerful compute capabilities for a wide range of workloads deployed across cloud, data center, and edge; including improved performance for…
…It also has delivered strong results in trusted third-party benchmarks such as MLPerf and SemiAnalysis InferenceX , reinforcing its position as a production-grade inference platform. Dynamo can boost the number of…
…Skills serve as a packaging mechanism, dynamically loading the correct procedural context and improving agent performance on specific tasks. This post outlines core NVIDIA cuOpt agent skills , their significance, and how they…
…For decades, unlocking reservoir potential has relied on experts performing essential and time-intensive manual workflows. As data complexity grows, the gap between machine speed and human bandwidth has become a primary…
…The collector toolchain benchmarks every primitive across supported quantization modes, batch sizes, sequence lengths, and GPU counts, and logs results to a silicon-calibrated performance database. When collected data isn’t available…
…The benchmark is performed using batches of randomly sampled structures from the OMat24 dataset. Accelerating OLED molecular and immersion cooling fluids discovery NVIDIA customers are achieving groundbreaking results with ALCHEMI’s new…
…In the previous post, Build High-Performance Vision AI Pipelines with NVIDIA CUDA-Accelerated VC-6 , this was described as the data-to-tensor gap—a performance mismatch between AI pipeline stages…
…Holoscan Reference Applications Check out and share reference applications, operators, tutorials, and benchmarks on the HoloHub. Learn About the Latest Release Jump into the latest features and improvements for the Holoscan SDK…
…to deployment—to achieve state-of-the-art performance and localized AI capabilities. This post walks through the joint engineering effort and shares benchmarks for the speed-ups achieved on the NVIDIA…
…Evaluation demonstrates significant accuracy gains (from ~20% to ~60%) for incident summary prediction and root-cause resolution, with ongoing robustness improvements via tool-calling benchmarks, LLM-as-a-judge safety checks, controlled…