How to Understand Nvidia's Role in Artificial Intelligence

NVIDIA is… having a moment. Their shocking revenue (and profit) growth over the last 6 months is literally unprecedented for a business of their size. But why is it happening? The obvious answer is LLM mania and the release of OpenAI's ChatGPT. But why is *that* happening now? And why does all the value seem to accurue to NVIDIA compared to all other GPU companies and suppliers? It's the perfect storm of these factors: 1. 🔋 OpenAI bet big on the transformer architecture in 2019, which just became commercially viable After Google's 2017 research paper "Attention is all you Need", the AI world discovered a new mechanism that could dramatically improve the output from language models: the Transformer. By 2019, OpenAI had bet the farm to this new (but expensive!) approach, raising billions of dollars and shifting their efforts to a Generative Pre-Trained Transformer model (GPT). Over the next 4 years, models went from terrible, to promising, to shockingly good. And plenty of other companies entered the LLM race too. 2. 💻 Turns out, LLMs need an astonishing amount of compute LLMs have a huge tradeoff. They can require hundreds of millions of dollars to train the models and huge amounts of data. NVIDIA had been working on just the right chip architecture (A100 and H100s) for the last 5 years to enable all of this. But it doesn't just take compute... 3. 🔌 LLMs also need super fast networking and on-chip memory Models like GPT-4 are so large that they can't be stored in the memory of a single GPU. They need to be spread across multiple GPUs (and multiple racks of GPUs!) in order to train. This requires high-bandwidth connection between GPUs and racks in order to all work like "one computer". In Jensen's words, "the data center is the computer." NVIDIA bought Mellanox a few years ago, and are the only provider in the market of InfiniBand networking. Infiniband is uniquely well-suited for this, with speeds of 3200 GB per *second*! Now, NVIDIA's integrated networking and high-bandwidth memory connected to the H100 GPUs make for a very nice solution. 4. 💾 NVIDIA's software stack (CUDA) is the standard for the entire ecosystem of AI development Programming for parallel execution is hard. NVIDIA has spent 15+ years building the entire software stack (compiler, programming language, dev tools, libraries, etc.) to make it doable. Their belief was that accelerated computing could happen much faster if you provide the right tools. Now, over 4 million developers use CUDA, providing a meaningful switching cost to move off of NVIDIA as an AI developer. 5. 🌕 As luck would have it, NVIDIA has reserved a huge chunk of TSMC's capacity to make chips like this. Interestingly, this actually pre-dates the AI boom (partially from crypto mining!) but either way, this advantage belongs to NVIDIA at the moment. This is just part of the story. For the rest, check out this Acquired episode! #ai https://lnkd.in/g-Aygazn

Nvidia’s dominance today isn’t just about the H100 chip — it’s the result of multi-decade platform engineering across hardware, software frameworks, and tight integration with the future of AI workloads. They systematically built and continue to defend that edge: 1️⃣ CUDA Lock-In at the Developer Level Today, every major deep learning framework — TensorFlow, PyTorch, JAX — is deeply optimized for CUDA, creating enormous inertia against switching. 2️⃣ Vertical Integration from Silicon to Cloud DGX systems (bundling H100s, NVLink, and Mellanox networking) offer full-stack optimization. Nvidia controls not just training chips, but high-bandwidth interconnects, model parallelism frameworks, and enterprise-ready AI infrastructure (DGX Cloud). 3️⃣ AI Workload-Specific Optimization Hopper was tuned for transformer models — custom Tensor Cores, FP8 precision, sparsity support — years before general-purpose chips adapted. Architecture decisions at Nvidia are increasingly model-first, not architecture-first. 4️⃣ Own the Inference Stack Too TensorRT and Triton Inference Server form a production-grade deployment layer, optimizing models post-training for latency, throughput, and cost — critical as AI workloads shift to inference at scale. 5️⃣ Closed-Loop Research Collaboration Unlike commodity chipmakers, Nvidia co-engineers future architectures with hyperscalers (e.g., OpenAI, DeepMind, Meta AI) before models are published. This feedback loop compresses iteration cycles and keeps Nvidia tuned to upcoming workload demands 12–24 months ahead. 6️⃣ Ecosystem Expansion into Vertical AI Domains Frameworks like Omniverse (simulations), Isaac (robotics), and Clara (healthcare AI) position Nvidia to dominate not just AI infrastructure, but domain-specific AI applications. 🏁 I still wonder whether Nvidia’s valuation is truly stretched — or simply a glimpse of a much bigger future.

NVIDIA's $7B Mellanox acquisition was actually one of tech's most strategic deals ever. The untold story of the most important company in AI that most people haven't heard of Most people think NVIDIA = GPUs. But modern AI training is actually a networking problem. A single A100 can only hold ~50B parameters. Training large models requires splitting them across hundreds of GPUs. Enter Mellanox. They pioneered RDMA (Remote Direct Memory Access) which lets GPUs directly access memory on other machines with almost no CPU overhead. Before RDMA, moving data between GPUs was a massive bottleneck. The secret sauce is in Mellanox's InfiniBand. While Ethernet does 200-400ns latency, InfiniBand does ~100ns. For distributed AI training where GPUs constantly sync gradients, this 2-3x latency difference is massive. Mellanox didn't just do hardware. Their GPUDirect RDMA software stack lets GPUs talk directly to network cards, bypassing CPU & system memory. This cuts latency another ~30% vs traditional networking stacks. NVIDIA's master stroke: Integrating Mellanox's ConnectX NICs directly into their DGX AI systems. The full stack - GPUs, NICs, switches, drivers - all optimized together. No one else can match this vertical integration. The numbers are staggering: - HDR InfiniBand: 200Gb/s per port - Quantum-2 switch: 400Gb/s per port - End-to-end latency: ~100ns - GPU memory bandwidth matching: ~900GB/s Why it matters: Training SOTA scale models requires: - 1000s of GPUs - Petabytes of data movement - Sub-millisecond latency requirements Without Mellanox tech, it would take literally months longer. The competition is playing catch-up: - Intel killed OmniPath - Broadcom/Ethernet still has higher latency - Cloud providers mostly stuck with RoCE NVIDIA owns the premium AI networking stack Looking ahead: CXL + Mellanox tech will enable even tighter GPU-NIC integration. We'll see dedicated AI networks with sub-50ns latency and Tb/s bandwidth. The networking advantage compounds. In the AI arms race, networking is the silent kingmaker. NVIDIA saw this early. The Mellanox deal wasn't about current revenue - it was about controlling the foundational tech for training next-gen AI. Next time you hear about a new large language model breakthrough, remember: The GPUs get the glory, but Mellanox's networking makes it possible. Sometimes the most important tech is invisible.