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NVIDIA — Presentation deck (company IR deck)
NVIDIA (NVDA) · 12 slides · Filed Oct 20, 2026
Every slide of this earnings presentation, with the text as published. Open the original deck ↗ · All NVDA deck slides
Slide 1 — SCALING AI AND USING IT
Bryan Catanzaro; September 9, 2021 SCALING AI AND USING IT
Slide 2 — Forward-looking statements
2 Forward-looking statements Except for the historical information contained herein, certain matters in this presentation including, but not limited to, statements as to: language model complexity doubling every two months; expected timing for 100T parameter single model; benefits, cost and impact of language models and few-shot learning; Megatron scaling on DGX SuperPoD; reinventing NVIDIA DLSS with deep learning; AI growing at a super-exponential rate; NVIDIA building the software and systems to create the most important AI; NVIDIA building advanced AI to transform our core business; and other predictions and estimates are forward- looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. These forward-looking statements and any other forward- looking statements that go beyond historical facts that are made in this presentation are subject to risks and uncertainties that may cause actual results to differ materially. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences and demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems and other factors. NVIDIA has based these forward-looking statements largely on its current expectations and projections about future events and trends that it believes may affect its financial condition, results of operations, business strategy, short- term and long-term business operations and objectives, and financial needs. These forward-looking statements are subject to a number of risks and uncertainties, and you should not rely upon the forward-looking statements as predictions of future events. The future events and trends discussed in this presentation may not occur and actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. Although NVIDIA believes that the expectations reflected in the forward-looking statements are reasonable, the company cannot guarantee that future results, levels of activity, performance, achievements or events and circumstances reflected in the forward-looking statements will occur. Except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances. For a complete discussion of factors that could materially affect our financial results and operations, please refer to the reports we file from time to time with the SEC, including our Annual Report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports we file with the SEC are posted on our website and are available from NVIDIA without charge.
Slide 3 — LARGE LANGUAGE MODELS
3 LARGE LANGUAGE MODELS AlexNet ResNet BERT GPT-2 Megatron-GPT2 Turing NLG Megatron-BERT GPT-3 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 2012 2013 2014 2016 2017 2018 2020 Petaflop/s - Days Exploding Model Complexity Doubling Every 2 Months PetaFLOPS - Days @ctnzr Compute required to train 175B OpenAI GPT-3 314 ZettaFLOP for training (3640 PFLOP/s * day) 100T parameter single model by 2023 Language Models Constrained by Economics
Slide 4 — TOWARDS THE $1B MODEL
4 TOWARDS THE $1B MODEL The value of few-shot learning English: I live in California. Spanish: Yo vivo en California. English: I work at NVIDIA. Spanish: Yo trabajo en NVIDIA. English: I believe in science. Spanish: Yo creo en la ciencia. Example: Few-shot translation This model was trained on general text from the internet — yet give it a few examples and ask it to do translation, and it can translate. Language describes all human activity. These models are expensive (GPT-3 cost $12M to train) But their value is great Generalized intelligence with few-shot learning What would it look like to build a $1B model? It would need to reinvent an entire company
Slide 5 — MEGATRON SCALING
MEGATRON SCALING ON DGX SUPERPOD All of NVIDIA’s HW and SW working together CUDA, CUDA-X AI, NVSwitch, DGX SuperPod, NCCL, CUBLAS, CUDNN Trained with PyTorch Language models useful for NVIDIA’s own products Case Hidden Size Number of Layers Model Parallel Size Number of GPUs 1T 25600 128 512 3072 530B 20480 105 280 2520 145B 12288 80 64 1536 39B 8192 48 16 512 7.5B 4096 36 4 128 0 100 200 300 400 500 600 0 500 1000 1500 2000 2500 3000 3500 PetaFLOP/s GPU Count Sustained performance Linear scaling 502 petaFLOP/s sustained at 3072 GPUs 52% of tensor-core peak 163 Tflops/GPU
Slide 6 — NVIDIA DLSS
6 NVIDIA DLSS Reinventing a Core NVIDIA Business with Deep Learning VS Supercomputer Rendered 128MPixel (16K) Ground Truth 4MPixel (1440P) Ray Tracing 8MPixel (4K) Deep Learning Super Sampling @ctnzr
Slide 7 — NATIVE 4K
7 108 141 NATIVE 4K DLSS 4K
Slide 8 — DLSS OFF
8 DLSS OFF DLSS ON 60 100 NATIVE 4K DLSS 4K 108 141
Slide 9 — not to use it.”
9 “ DLSS is impressive to the point where I believe you'd be nuts not to use it.” — Digital Foundry “ The upscaling power of this new AI driven algorithm is extremely impressive… it’s basically a free performance button.” — Hardware Unboxed
Slide 10 — CREATING A SELF-DRIVING
10 HD Map Data Factory Synthetic Data Drive Sim CREATING A SELF-DRIVING CAR PLATFORM, INFRASTRUCTURE, AND SERVICE Map Generation Perception AI Training Driving AI Training Model Data Model Data Data Data Model DRIVE AV Model AV Map
Slide 11 — SCALING AI AND USING IT
11 SCALING AI AND USING IT AI is growing at a super-exponential rate NVIDIA is building the software and systems to create the most important AI We are applying advanced AI to transform and grow our core business We offer the tools for other companies to do so as well