Build Next-Gen Physical AI with Edge‑First LLMs for Autonomous Vehicles and Robotics | NVIDIA Technical Blog
… He works on end-to-end LLM software development, performance measurements, analysis and improvements for x86 64 and aarch64 platforms. …
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What metrics should you measure for LLM inference performance?
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 BlogHow do you calculate required server capacity for peak LLM request volumes?
To calculate the required infrastructure for a given LLM application, we need to identify the following constraints: Latency type and maximum value. This typically depends on the nature of the applications. For example, for chat applications with live interactive responses, keep the average time to first token at or below 250 ms to ensure responsiveness. Planned peak requests/s. Account for how many concurrent requests the system is expected to serve. Note that this isn’t the same as the number of concurrent users, because not all will have an active request at once. Using this information,
LLM Inference Benchmarking: How Much Does Your LLM Inference Cost? | NVIDIA Technical BlogWhat formulas determine cost per token and yearly depreciation for LLM inference?
To estimate the amount of hardware and software licenses required and the associated cost, follow these steps and a hypothetical example First, collect and identify the cost information corresponding to both hardware and software. Next, calculate the total cost following the steps: Number of servers is calculated as the number of instances times the GPUs per instance, divided by the number of GPUs per server. Yearly server cost is calculated as the initial server cost divided by the depreciation period (in years), adding the yearly software licensing and hosting costs per server. Total cost is
LLM Inference Benchmarking: How Much Does Your LLM Inference Cost? | NVIDIA Technical BlogLLM Inference Benchmarking: How Much Does Your LLM Inference Cost? | NVIDIA Technical Blog
… He has contributed to production applications of LLMs covering RAG systems, optimization of inference servers, pretraining of LLMs from scratch, custom evaluation of LLMs, or quantization using FP8 formats. …
Automating Inference Optimizations with NVIDIA TensorRT LLM AutoDeploy | NVIDIA Technical Blog
… Discuss 0 Discuss 0 Tags Agentic AI / Generative AI | Developer Tools & Techniques | MLOps | General | Blackwell | TensorRT | TensorRT-LLM | Intermediate Technical | Tutorial | AI Inference | featured | Inference Performance | LLMs | PyTorch About the Authors About Lucas Liebenwein Lucas Lieben… …
How Small Language Models Are Key to Scalable Agentic AI | NVIDIA Technical Blog
… NVIDIA offers a range of tools, including NVIDIA NeMo and NVIDIA Nemotron models, to support the development of heterogeneous AI systems that combine SLMs and LLMs, enabling enterprises to improve efficiency, reduce costs, and scale responsibly. …
Winning a Kaggle Competition with Generative AI–Assisted Coding | NVIDIA Technical Blog
… I will run the code and share the plots and text back with you.” If using LLM with code execution like Claude Code, then you can ask the LLM to write and run its own code to understand the data. “Please write and run EDA code to understand the CSV files train.csv and test.csv” Step 2: LLM agents bu… …
Pruning and Distilling LLMs Using NVIDIA TensorRT Model Optimizer | NVIDIA Technical Blog
… Discuss 0 Discuss 0 Tags Developer Tools & Techniques | General | NeMo | TensorRT | Beginner Technical | Intermediate Technical | Tutorial | featured | LLM Techniques | LLMs | Model Optimizer About the Authors About Max Xu Max Xu is a senior technical lead at NVIDIA specializing in AI training and … …
Advancing Emerging Optimizers for Accelerated LLM Training with NVIDIA Megatron | NVIDIA Technical Blog
… Get started with emerging optimizers for LLM training Higher-order optimizers like Muon are proving essential for pushing the boundaries of LLM training efficiency. …
MLOps – NVIDIA Technical Blog
… 9 MIN READ Jan 08, 2026 Accelerating LLM and VLM Inference for Automotive and Robotics with NVIDIA TensorRT Edge-LLM Large language models LLMs and multimodal reasoning systems are rapidly expanding beyond the data center. …
Build a Retrieval-Augmented Generation (RAG) Agent with NVIDIA Nemotron | NVIDIA Technical Blog
… This example shows the ChatNVIDIA LangChain connector using NVIDIA NIM. from langchain nvidia ai endpoints import ChatNVIDIA LLM MODEL = "nvidia/nvidia-nemotron-nano-9b-v2" llm = ChatNVIDIA model=LLM MODEL, temperature=0.6, top p=0.95, max tokens=8192 To ensure the quality of the LLM-based applicat… …
Post-Training Quantization of LLMs with NVIDIA NeMo and NVIDIA TensorRT Model Optimizer | NVIDIA Technical Blog
… The following Python commands show how to build a TensorRT-LLM engine and pass an example prompt through the model. from nemo.export.tensorrt llm import TensorRTLLM trt llm exporter = TensorRTLLM model dir=”path/to/trt llm engine” trt llm exporter.export nemo checkpoint path=”path/to/model qnemo”, … …