The engine powering modern AI
NVIDIA dominates AI infrastructure with both hardware (GPUs, DGX systems) and software (CUDA, TensorRT, NeMo, Triton). From training foundation models on H100/B200 clusters to deploying inference with TensorRT-LLM, NVIDIA's stack powers the majority of AI workloads globally. Their Blackwell architecture (B200, GB200) represents the latest generation, delivering up to 4x inference performance over Hopper.
B200 and GB200 NVL72 deliver up to 20 petaflops FP4 inference performance per rack with 192GB HBM3e per GPU
The industry-standard parallel computing platform with 4M+ developers, 800+ GPU-accelerated libraries
High-performance inference engine optimized for large language models with INT4/FP8 quantization and KV-cache optimization
End-to-end framework for building, training, and deploying custom LLMs, multimodal models, and speech AI
NVIDIA integrated GPU-accelerated Omniverse libraries into NVIDIA Agent Toolkit, giving AI coding agents skills to generate and validate 3D assets for physical AI simulation.
$0
Open source tools, no GPU included
From $4,500/GPU/year
Per-GPU annual license
Production-grade model serving supporting TensorRT, ONNX, PyTorch, TensorFlow with dynamic batching
Pre-optimized inference microservices for deploying AI models as API endpoints — deploys in minutes
Multi-cloud AI supercomputing platform providing dedicated NVIDIA GPU clusters with turnkey infrastructure
Enterprise software suite with security, manageability, and support for production AI deployments
NVIDIA expanded the NVIDIA Agent Toolkit for engineering by adding PhysicsNeMo physics-AI libraries and updated CUDA-X libraries as callable tools and skills for autonomous AI agents.
NVIDIA released DeepStream 9.1 with 13 agentic skills, Multi-View 3D Tracking, AutoMagicCalib, and support for NVIDIA JetPack 7.2.
Compare NVIDIA GPUs from RTX 4060 Ti to GB200 NVL72 for AI training and inference workloads.
From $37,000/mo
Monthly commitment, multi-cloud