NVIDIA AI Platform
GPU compute & CUDA ecosystem
General Infrastructure
NVIDIA AI Platform is the foundational GPU compute ecosystem that powers the vast majority of modern AI and deep learning workloads. At its core are CUDA, cuDNN, and TensorRT — the parallel computing toolkit, deep learning library, and inference optimizer that nearly every ML framework depends on.
ML engineers, data scientists, and MLOps teams use the NVIDIA platform because virtually all deep learning roads lead through CUDA. Whether you are training a custom model from scratch or deploying an open-source LLM in production, the NVIDIA stack provides the low-level acceleration layer.
Beyond raw compute, NVIDIA offers NeMo for LLM training and customization, Triton Inference Server for serving models at scale, and RAPIDS for GPU-accelerated data science and feature engineering.
nvidia-sminvcc --versiondocker pull nvcr.io/nvidia/pytorch:24.01-py3
docker run --gpus all -it nvcr.io/nvidia/pytorch:24.01-py3Pricing: CUDA, cuDNN, and TensorRT are free. GPU hardware costs vary — cloud H100 instances range from $2-4/hr. NGC containers are free to pull.
nvidia-smi to monitor GPU utilization during training. If utilization is below 80%, your data pipeline is likely the bottleneck — look into NVIDIA DALI for GPU-accelerated data loading.Be the first to share a NVIDIA AI Platform case study and get discovered by clients.
Submit a case studyThought leaders
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Eureka Labs
Founding member of OpenAI and former Director of AI at Tesla where he led the Autopilot computer vision team. PhD from Stanford under Fei-Fei Li. Created Stanford's CS 231n course which grew from 150 to 750 students. Founded Eureka Labs for AI education. Released nanoGPT and nanochat as open-source educational tools. One of the most influential AI educators, with his Neural Networks: Zero to Hero series widely considered the gold standard.
Modal Labs
Founder of Modal Labs, the high-performance serverless cloud for developers that reached unicorn status ($1.1B valuation) in September 2025. Previously spent 7 years at Spotify where he built the music recommendation system and created Luigi, the popular workflow scheduler. Also built Annoy, one of the first open-source vector databases based on ANN search.
Groq
CEO and founder of Groq, creator of the LPU (Language Processing Unit) that delivers up to 18x faster inference than traditional GPUs. Creator of Google's TPU (Tensor Processing Unit). A high school dropout who became one of the most influential figures in AI hardware. Groq's technology was valued at ~$20B in Nvidia's December 2025 licensing deal.
Weights & Biases / CoreWeave
CEO and co-founder of Weights & Biases, the MLOps platform used by organizations like OpenAI, Salesforce, and Microsoft. Co-founded W&B with Chris Van Pelt and Shawn Lewis in 2017. What began with experiment tracking at OpenAI grew into an end-to-end MLOps platform used by millions. W&B was acquired by CoreWeave in March 2025 for $1.7B.
NVIDIA
VP of AI/ML for IT at NVIDIA, leading enterprise agentic AI initiatives and adaptive systems. Deep expertise in building and deploying AI platforms at scale within one of the world's most important AI companies.
Caltech
Bren Professor at Caltech specializing in neural operators, scientific AI, tensor methods, and deep learning. One of the most cited AI researchers working at the intersection of AI and scientific computing.
AMD
Global Software Analytics Leader at AMD, working on agent architecture, LLM coordination, and digital transformation. Deep expertise in how AI hardware and software work together for enterprise-scale agent deployments.
Google DeepMind
CEO and co-founder of Google DeepMind, the world's leading AI research lab. Led breakthroughs including AlphaGo (defeating world Go champion), AlphaFold (solving protein folding), and pioneering AGI research. Nobel Prize-winning contributions to computational biology.
NVIDIA
Senior Research Scientist at NVIDIA leading the AI Agents initiative. Creator of Voyager (first LLM-powered agent in Minecraft) and contributor to Project GR00T for humanoid robots. One of AI's most influential voices on social media with deep expertise in foundation models and embodied AI.
NVIDIA
VP of Applied Deep Learning Research at NVIDIA. Leads teams building NVIDIA's AI products including NeMo framework. Early pioneer in GPU-accelerated deep learning.
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