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Nvidia

Nvidia, maker of AI chips and the Nemotron models, tracked through the same six lenses as every company on AI Adoption.
Its best model, Nemotron 3 Ultra, scores 146.0 on the Epoch Capabilities Index, #9 of 10, 22.8 behind Anthropic. It released 3 notable models in 2025, #8 of 10 by count. Its largest training run with a published estimate, Nemotron-4 340B, used 1.8×10²⁵ FLOP. On one reliability test, AA-Omniscience, its best model's hallucination rate was 83%, #5 of 9.
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7 ledger entries4 charts name NvidiaLatest entry Jun 2026Checked 20 Oct 2026

Nvidia through six lenses

How the lenses work →
The same six lenses, in the same order, on every company page. Each shows one comparable number where a source measures several companies the same way, its rank, and when it last changed.
01Capability#9 of 10
How good are its best models?
146.0
Epoch Capabilities Index score of its best model, Nemotron 3 Ultra (Jun 2026)
Anthropic168.8OpenAI168.8Google166.4Meta161.8Nvidia146.0
Epoch combines many benchmarks into one scale. 90% interval for this model: 143.7 to 148.2.
Independent measureChanged Jun 2026Epoch AI data →
02Output#8 of 10
How much does it ship?
3
Notable AI models released in 2025
OpenAI21Google15Alibaba11Anthropic7Nvidia3
30 notable models from 2014 to 2025 and 2 in 2026 so far. Model record →
Independent measureChanged 2025View chart →
03Money
How much does it earn?
Not measured yet
No AI revenue figure for Nvidia in Epoch AI's company data
Epoch AI tracks revenue for AI-first companies such as OpenAI, Anthropic, Mistral AI, DeepSeek.
How a lens gets a number →
04Compute#6 of 10
How much compute and energy goes into its AI?
1.8×10²⁵
FLOP: its largest training run with a published estimate, Nemotron-4 340B (Jun 2024)
OpenAI9.4×10²⁶xAI5×10²⁶Meta5.2×10²⁵Google5×10²⁵Nvidia1.8×10²⁵
Largest estimated training cost: $21M (Nemotron-4 340B), Epoch AI. Model record →
Ranked among the 10 tracked companies. Only runs with a published estimate count, so a company that discloses less can rank lower.
Independent estimateChanged Jun 2024View chart →
05Use
Who uses it, and for what?
Not measured yet
No Nvidia service in the Epoch AI / Ipsos poll
The poll asks about ChatGPT, Claude, Gemini, Copilot, Grok, Meta AI and DeepSeek.
How a lens gets a number →
06Safety & policy#5 of 9
How reliable is it, and how is it governed?
83%
hallucination rate of its most reliable model on AA-Omniscience, NVIDIA Nemotron Nano
xAI22%Anthropic26%Google50%DeepSeek82%Nvidia83%
Lower is better. Ranked among the tracked companies; the test covers 26 models. One reliability test: safety frameworks, evaluations and regulatory actions are logged as entries.
Independent measureChanged 2026View chart →

The ledger

What gets an entry →
An entry goes in only if it changes a number or a position on one of the six lenses. If it moves nothing, it is not news here. Newest first.

  1. June 2026

    1. 4 Jun 2026CapabilityNewNemotron 3 Ultra scored 146.0 on the Epoch Capabilities Index, its best model#9 of the 10 tracked companies' best models; Anthropic's best scored 168.8. Epoch's 90% interval: 143.7 to 148.2.Independent measureEpoch AI, Benchmarking HubEpoch AI data →#9
    2. 4 Jun 2026OutputNewNemotron 3 Ultra released, trained with an estimated 6.6×10²⁴ FLOPLess training compute than Llama-3.1-Nemotron-70B-Instruct (7.9×10²⁴).Independent estimateEpoch AIView chart →6.6×10²⁴

  2. 2026 tests

    1. 2026 testSafety & policyNewIts most reliable model on AA-Omniscience, NVIDIA Nemotron Nano, had a 83% hallucination rate#5 of 9 tracked companies; xAI's best was 22%. Rates across the 26 models tested ran from 22% to 94%.Independent measureArtificial AnalysisView chart →#5 of 9

  3. 2017–2025

    1. 2017–2025ComputeNewNvidia's A100 has trained 84 notable AI models since 2017, the most of any chipThe older V100 follows with 69 and Google's TPU v3 with 44.Independent estimateEpoch AIView chart →84

  4. 2025, full year

    1. 2025OutputNewReleased 3 notable AI models in 2025, #10 by countOpenAI 21, Google 15, Alibaba 11. Epoch AI counts a model as notable if it was state of the art, highly cited or historically significant.Independent measureEpoch AIView chart →#10

  5. October 2024

    1. 12 Oct 2024OutputNewLlama-3.1-Nemotron-70B-Instruct released, trained with an estimated 7.9×10²⁴ FLOPLess training compute than Nemotron-4 340B (1.8×10²⁵).Independent estimateEpoch AIView chart →7.9×10²⁴

  6. June 2024

    1. 14 Jun 2024OutputNewNemotron-4 340B released, trained with an estimated 1.8×10²⁵ FLOPIndependent estimateEpoch AIView chart →1.8×10²⁵

Nvidia's model record

Download the CSV →
Every notable model Epoch AI has logged for Nvidia: 32 in all, 30 from 2014 to 2025 and 2 in 2026 so far. Training compute, cost and hardware are Epoch's estimates, shown only where it has one.
Notable models, 2014–202530#5 of 10 companies · Epoch AI's count, as in chart 1.1.7
Frontier models4#4 of 10 companies · marked frontier in Epoch AI's database
Open weights83%15 of 18 notable models since 2023
Largest estimated training cost$21M#6 of 10 companies · Nemotron-4 340B (Jun 2024), 2023 dollars
Largest training cluster6,144#5 of 8 companies · NVIDIA H100 SXM5 80GB · Nemotron-4 340B
Notable models per year* 2026 to 19 Oct 2026
2014: 020142015: 02016: 020162017: 112018: 1120182019: 442020: 020202021: 552022: 3320222023: 442024: 9920242025: 332026: 222026*
Notable models since 2023, newest first18 models
ReleasedModelAccessTraining computeEst. costTraining hardware
4 Jun 2026Nemotron 3 UltraOpen weights6.6×10²⁴——
11 Mar 2026Nemotron 3 SuperOpen weights———
15 Dec 2025Nemotron 3-Nano-30B-A3BOpen weights4.8×10²³—NVIDIA H100 SXM5 80GB
22 Mar 2025Diffusion RendererOpen weights——32 NVIDIA A100
20 Jan 2025Eagle 2Open weights4.7×10²²—256 NVIDIA H100 SXM5 80GB
31 Dec 2024STORM-B/8Open weights——16 NVIDIA A100
5 Dec 2024NVILA 15BOpen weights——128 NVIDIA H100 SXM5 80GB
25 Nov 2024Fugatto 1Unreleased——32 NVIDIA A100
22 Oct 2024NVLM-X 72BUnreleased3×10²⁴—128 NVIDIA H100 SXM5 80GB
22 Oct 2024NVLM-H 72BUnreleased3×10²⁴—128 NVIDIA H100 SXM5 80GB
Show all 18 models since 2023
22 Oct 2024NVLM-D 72BOpen weights3×10²⁴—128 NVIDIA H100 SXM5 80GB
12 Oct 2024Llama-3.1-Nemotron-70B-InstructOpen weights7.9×10²⁴——
14 Jun 2024Nemotron-4 340B frontierOpen weights1.8×10²⁵$21M6,144 NVIDIA H100 SXM5 80GB
3 May 2024VILA1.5-13BOpen weights2.3×10²¹—128 NVIDIA A100
12 Dec 2023VILA-13BOpen weights2.3×10²¹—128 NVIDIA A100 SXM4 80 GB
15 Nov 2023Nemotron-3-8BOpen weights1.8×10²³$214K1,024 NVIDIA A100
9 Feb 2023ProteinDTOpen weights———
15 Jan 2023Nucleotide TransformerOpen weights8.1×10²¹$51K128 NVIDIA A100
Data: Epoch AI, Data on AI Models, snapshot 19 Oct 2026, CC BY 4.0. Organisations are grouped into companies as in chart 1.1.7 (for example, Google DeepMind and Google Brain count as Google). Nvidia's models as CSV.

Nvidia beside the others

All 10 companies →
The same rows for every company. A dash means no source measures it for that company yet: gaps are shown, never filled with a guess. The small number is the rank among the companies in the full table that the source covers.
LensNvidiaOpenAIAnthropicGooglexAIMetaMicrosoftDeepSeekAlibabaMistral AI
CapabilityEpoch Capabilities Index, its best model · data146.0#9168.8#1168.8#1166.4#3156.6#6161.8#4130.5#10155.9#7157.4#5150.0#8
OutputNotable AI models released in 2025 · chart3#821#17#415#25#54#60#104#611#31#9
MoneyAnnualized revenue, latest report · data—$50B#2Sep 2026$65B#1Jul 2026—$428M#5Sep 2025——$1B#3Sep 2026—$1B#3Sep 2026
ComputeLargest training run with a published estimate, FLOP · chart1.8×10²⁵#6Nemotron-4 340B, Jun 20249.4×10²⁶#1GPT-6 Astra, Sep 20263.4×10²⁵#5Claude 3.7 Sonnet, Feb 20255×10²⁵#4Gemini 1.0 Ultra, Dec 20235×10²⁶#2Grok 4, Jul 20255.2×10²⁵#3Llama 4 Behemoth (preview), Apr 20258.6×10²³#9Megatron-Turing NLG 530B, Oct 2021 (with Nvidia)9.7×10²⁴#8DeepSeek-V4-Pro, Apr 20261.5×10²⁵#7Qwen3-Max, Sep 20257.7×10²³#10Mixtral 8x7B, Dec 2023
UseUS adults who used its assistant in the past week, Mar 2026 · data—31.0%#13.0%#620.9%#24.5%#57.5%#410.5%#30.6%#7——
Safety & policyLowest hallucination rate of its models (lower is better) · chart83%#589%#826%#250%#322%#187%#7—82%#489%#884%#6
More measures · Epoch AI data
Text Arenabest score, Mar 2026 · data—1,481#41,503#11,494#31,495#21,335#8—1,424#61,449#51,416#7
Valuationlatest closed round · data—$852.0B#2$965.0B#1—$230.0B#3——$50.0B#4—$24.0B#5
Equity raisedin all · data—$182.8B#1$139.4B#2—$37.0B#3——$7.5B#4—$6.5B#5
Stafflatest report · data—4,500#23,000#46,000#1300#63,400#3—300#6—350#5
AI chips ownedH100-equivalents · data———5.47M#10.55M#41.95M#33.32M#2———
Compute availableH100-equivalents, end of 2025 · data—1.74M#11.19M#31.58M#20.63M#51.00M#4————
Weekly use at workemployed US adults, Jul 2026 · data—33.7%#17.7%#524.2%#23.3%#69.8%#414.8%#3———
Model record · Epoch AI estimates
Notable models, 2014–2025Epoch AI's count, as in chart 1.1.730#561#313#7194#17#887#242#47#827#66#10
Frontier modelsmarked frontier in Epoch AI's database4#415#23#533#13#55#33#50#80#80#8
Open weightsshare of its notable models since 202383%15 of 184%2 of 570%0 of 2214%8 of 5711%1 of 973%19 of 2633%2 of 6100%10 of 1075%24 of 3267%4 of 6
Largest estimated training cost2023 US dollars, among models Epoch has costed$21M#6Nemotron-4 340B$366M#2GPT-4.5$26M#5Claude 3.5 Sonnet$31M#4Gemini 1.0 Ultra$388M#1Grok 4$53M#3Llama 3.1-405B$4M#9Megatron-Turing NLG 530B$7M#8DeepSeek-R1 (May 2025)$109K#10PLUG$14M#7Mistral Large
Largest training clusterchips for one model, where Epoch records it6,144#5Nemotron-4 340B100,000#2GPT-6 Astra—57,000#3Gemini 1.0 Ultra200,000#1Grok 432,000#4Llama 4 Behemoth (preview)4,480#6Megatron-Turing NLG 530B2,048#7DeepSeek-V3 (Mar 2025)512#8CogView—
Charts on AI Adoptionthat name the company4332223840471

Every chart that names Nvidia

All charts →
4 charts across 2 hubs name Nvidia or one of its models. Each has its source and a CSV of its data.
AI Infrastructure3 charts
  1. 8484 notable AI models have been trained on Nvidia A100 chips, more than on any other accelerator
  2. 17.1MAI chips worldwide reached 17.1 million H100-equivalents by end-2025; Nvidia supplied two-thirds
  3. 5,235 MWAI accelerator chips sold in 2023–24 draw an estimated 5,235 MW; Nvidia's account for half
Robots & Self-Driving Cars1 chart
  1. 1,727 hoursNvidia's 2025 PhysicalAI-AV dataset holds 1,727 hours of driving, the most of 13 benchmarks tracked

The other companies, same lens

AI Companies →
OpenAI168.8Epoch Capabilities Index65 ledger entriesOpen tracker →Anthropic168.8Epoch Capabilities Index61 ledger entriesOpen tracker →Google166.4Epoch Capabilities Index48 ledger entriesOpen tracker →xAI156.6Epoch Capabilities Index37 ledger entriesOpen tracker →Meta161.8Epoch Capabilities Index33 ledger entriesOpen tracker →Microsoft130.5Epoch Capabilities Index4 ledger entriesOpen tracker →DeepSeek155.9Epoch Capabilities Index35 ledger entriesOpen tracker →Alibaba157.4Epoch Capabilities Index33 ledger entriesOpen tracker →Mistral AI150.0Epoch Capabilities Index26 ledger entriesOpen tracker →

How the ledger works

  1. One lens for everyone. Six lenses, same order, same definitions, for every company. No company gets an extra lens for a product it wants to talk about.
  2. Changes, not news. An entry must move a number or a rank on a lens. Launch events, interviews and hires do not count unless they do.
  3. Evidence tier on every line. Independent measure, independent estimate or company data. A company's own claims are replaced when an independent score arrives.
  4. Gaps stay visible. If no source measures a lens for a company, the page says so.
Disclosure: AI Adoption uses AI tools in its research and editorial work, including products from some of the companies tracked here. No company has any say in its page or its ledger, and the same rules apply to every company. Editorial standards · Data sources
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