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#8 of 30 · Chipmaker

NVIDIA

Runs its own operations on 100% renewables; its chips are the industry's footprint.

Santa Clara, California·nvidia.com ·Verified Oct 16, 2026
BGood confidence
Index score
Good confidence

In plain words

How scores work

NVIDIA scores 63 out of 100 — middle of the pack. Some disclosure or clean-energy commitments, offset by rising emissions or blind spots. Confidence in this score is good confidence (B).

What helps

  • Efficiency · Publishes embodied carbon intensity per exaflop (0.66 g for HGX H100 vs 0.50 g for HGX B200) and claims up to 25× better inference efficiency per generation.
  • Carbon · 100% renewable electricity for offices and data centres under operational control in FY2025 (from 76%); SBTi-validated 50% Scope 1+2 cut by FY2030.
  • Transparency · Full sustainability report with limited assurance on the renewable share, product carbon footprints per GPU and Scope 3 by category.

What hurts

  • Efficiency · Efficiency gains are per unit of compute; absolute power keeps climbing, with a GB200 NVL72 rack drawing around 120 kW.
  • Carbon · Scope 3 jumped from 3.5 to 6.9 MtCO₂e in a year, mostly purchased goods — chip and system manufacturing.
  • Carbon · The electricity consumed by GPUs after sale is the real footprint: de Vries projected 85–134 TWh a year from NVIDIA servers by 2027.

What nobody knows

  • Per-prompt energy disclosed
  • Per-prompt water disclosed
  • Water-use effectiveness reported

NVIDIA makes the accelerators behind almost every company in this index. Its own offices and data centres reached 100% renewable electricity in FY2025 and it publishes a product carbon footprint for the H100 (about 164 kg CO₂e per GPU), but Scope 3 emissions doubled to 6.9 MtCO₂e in a year as Blackwell shipments ramped, and the lifetime electricity its sold GPUs consume — which de Vries projected at 85–134 TWh a year by 2027 — dwarfs anything on its own books.

Products
H100 / H200 · B200 / GB200 NVL72 · DGX Cloud · NIM · Nemotron models
Hosting
Own research clusters (Eos, Selene) plus DGX Cloud capacity leased from partners; chips fabricated by TSMC in Taiwan and Arizona.

Key figures

Per-prompt and fleet metrics. Hover a title for what it means; open “How we got this number” for the working.

How to read the labels:Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.Measured independentlyMeasured independently. Measured or estimated by researchers or analysts with no stake in the result.ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Not disclosedNot disclosed. The company publishes nothing on this and no defensible estimate exists.— hover any label.
Energy per promptElectricity used to answer one typical request. Under 0.3 Wh is good. A 10 W LED bulb uses 0.3 Wh in about two minutes.
0.30
Wh / typical prompt
≈ 25.0 smartphone charges per 1,000 prompts
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Epoch AI

ecogpt.co estimate; no company figure.

How we got this number

NVIDIA's Nemotron models are frontier-class and served through NIM on H100 and B200 systems; Epoch's 0.3 Wh for a comparable GPT-4o query is the anchor. Uncertainty roughly ±3×.

Water per promptWater evaporated cooling the servers for one request. Scope matters: on-site cooling only, or also the water used to generate the electricity (which adds ≈1.8 mL per Wh).
0.09
mL / typical prompt
≈ 0.18 500 mL bottles per 1,000 prompts
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Epoch AIMicrosoft

On-site cooling only, so comparable with Google and OpenAI's figures.

How we got this number

0.3 Wh per prompt × 0.3 L/kWh (estimated WUE for NVIDIA's own and partner-hosted clusters). Counting the water evaporated to generate the electricity adds ≈0.54 mL.

Carbon per promptGreenhouse gas from one request on the grid where it actually runs. Below 0.1 g is clean. A kilometre in a petrol car is about 170 g.
0.117
gCO₂e / typical prompt
≈ 0.69 km driven (petrol car) per 1,000 prompts
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Epoch AI

Location-based: what the grid actually emitted. Renewable certificates would bring the market-based figure near zero.

How we got this number

0.3 Wh × 390 gCO₂/kWh (US grid, location-based). NVIDIA's own sites are 100% renewable-matched, so its market-based figure is near zero.

Annual emissionsThe company's whole yearly footprint, in million tonnes of CO₂e. Clouds and device makers dwarf pure labs because they also count shops, logistics or products. Compare the trend, not the size.
6.90
MtCO₂e (FY2025, Scope 3)
Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.Data Center Dynamics

Scope 1: 13 kt. Scope 2 (market-based): 0.

Emissions trendDirection of travel against last year or the company's own baseline. Negative is good. Positive means the AI build-out is outrunning clean energy.
+97.0
% YoY Scope 3 (3.5 → 6.9 Mt, FY2025)
Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.Data Center Dynamics

Driven by purchased goods as Blackwell ramped.

Water per kWh (WUE)Litres of water consumed per kilowatt-hour of server electricity, across the fleet. 0.15 is excellent, 0.3 is good, 1.8 is the US data-centre average.
0.30
L/kWh
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.MicrosoftAmazon
How we got this number

No disclosure. NVIDIA's own clusters are liquid-cooled and DGX Cloud runs in partner hyperscale sites that report 0.15–0.30 L/kWh.

Clean electricityShare of electricity matched with renewable or carbon-free sources. Annual matching counts certificates bought anywhere; hourly matching on the same grid is far stricter.
100
% renewable electricity, operational control (FY2025)
Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.Data Center Dynamics

Mix of on-site renewables, utility tariffs, certificates and PPAs.

Hardware & materials

The chips, power, copper and future e-waste behind the fleet — the part sustainability reports leave out

Factors we use
AI chips in service
50,000
accelerators in NVIDIA's own clusters and DGX Cloud
≈ 694 racks of 72 GPUs
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.US Securities and Exchange CommissionData Center Dynamics

GPUs, TPUs or custom accelerators the company runs. Analyst figures are in H100-equivalents, company figures in physical chips.

How we got this number

Eos alone is 4,608 H100s and NVIDIA leases DGX Cloud capacity from partners; its own fleet is small next to what it ships — analysts estimated 1–1.5 million Hopper GPUs a quarter at the 2024 peak.

Power draw
94.0
MW (average draw)
≈ 70,500 US homes' worth of power
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Data Center Dynamics

Megawatts the AI fleet pulls. One megawatt runs roughly 750 US homes.

How we got this number

0.82 TWh a year ÷ 8,760 hours. Average draw implied by annual electricity; peak capacity is higher.

Electricity a year
0.82
TWh (FY2025, all operations)
≈ 78,095 US homes for a year
Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.Data Center Dynamics

Terawatt-hours per year. One TWh is a year of electricity for about 95,000 US homes.

How we got this number

821,200 MWh, up more than 200,000 MWh in a year.

Carbon baked into the hardware
25.7
ktCO₂e embodied in chips and servers
≈ 5,587 petrol cars driven for a year
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.US Securities and Exchange CommissionData Center DynamicsarXiv 2607.05408NVIDIA developer blog

CO₂e emitted making the chips and servers before they answer a single prompt.

How we got this number

50,000 accelerators × 514 kg CO₂e each (chip plus share of the host server). Emitted in manufacturing, before the first prompt is served.

Future e-waste
850
tonnes of servers to retire within ~5 years
≈ 4.7 empty Boeing 747s by weight
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.US Securities and Exchange CommissionData Center DynamicsNVIDIAThe Register

Tonnes of server hardware that will be retired in three to five years.

How we got this number

50,000 accelerators × 17 kg of server hardware each. Tomorrow's e-waste unless reused; generative AI could produce 2.5 Mt of it a year by 2030.

Copper in the build
2,538
tonnes of copper in the build
≈ copper for 30,578 electric cars
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Data Center DynamicsBHPS&P Global Market Intelligence

Tonnes of copper in cabling, busbars and transformers.

How we got this number

94 MW × 27 t of copper per MW (cabling, busbars, transformers).

Fossil generators on site
36.0
backup diesel generators (implied)
≈ 94 MW of backup capacity
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Data Center DynamicsLatitude MediaLatitude Media / Floodlight

Diesel backup units or gas turbines at the data centres, permitted or observed.

How we got this number

94 MW ÷ 2.6 MW per generator, the average size permitted at US data centres. Permit counts are not published fleet-wide; this is the number a fleet this size normally carries.

Hardware reused or recycled
—
No hardware reuse or e-waste figure published.
Not disclosedNot disclosed. The company publishes nothing on this and no defensible estimate exists.

Share of retired servers and parts given a second life.

Mineral depletion
—
Only Mistral has published a mineral-depletion figure.
Not disclosedNot disclosed. The company publishes nothing on this and no defensible estimate exists.

Kilograms of antimony-equivalent, the life-cycle unit for scarce minerals consumed.

Score breakdown

Each pillar is 0–100; weights are fixed across the index

Energy efficiency — How much electricity one answer costs.weight 20% · confidence B
70
  • Publishes embodied carbon intensity per exaflop (0.66 g for HGX H100 vs 0.50 g for HGX B200) and claims up to 25× better inference efficiency per generation. NVIDIA developer blog
  • Efficiency gains are per unit of compute; absolute power keeps climbing, with a GB200 NVL72 rack drawing around 120 kW. The Register
Carbon intensity — How clean that electricity is, and whether total emissions are rising.weight 25% · confidence B
60
  • 100% renewable electricity for offices and data centres under operational control in FY2025 (from 76%); SBTi-validated 50% Scope 1+2 cut by FY2030. Data Center DynamicsNVIDIA
  • Scope 3 jumped from 3.5 to 6.9 MtCO₂e in a year, mostly purchased goods — chip and system manufacturing. Data Center Dynamics
  • The electricity consumed by GPUs after sale is the real footprint: de Vries projected 85–134 TWh a year from NVIDIA servers by 2027. Cell Press via EurekAlert
Water — How much water the data centres consume, and whether it is replaced.weight 15% · confidence C
55
  • No fleet WUE; its own data-centre water use is small. Upstream, TSMC — which fabricates every NVIDIA data-centre GPU — consumed 129 million m³ of water in 2024. TSMC
Transparency — How much the company actually publishes.weight 20% · confidence B
68
  • Full sustainability report with limited assurance on the renewable share, product carbon footprints per GPU and Scope 3 by category. NVIDIAData Center Dynamics
  • No absolute figure for the lifetime energy of sold products, only an intensity target. Data Center Dynamics
Commitments — Whether it has dated, measurable climate and water targets.weight 10% · confidence B
60
  • SBTi: 50% absolute Scope 1+2 cut by FY2030; 75% cut in Scope 3 intensity of sold GPUs per PFLOP by FY2030; suppliers covering 80% of supply-chain emissions engaged. Data Center DynamicsNVIDIA
  • No absolute Scope 3 target, which is where 99% of its emissions sit. Data Center Dynamics
Conduct — How it behaves: permits, lawsuits, gas turbines, community water.weight 10% · confidence B
65
  • No environmental enforcement actions; publishes product carbon footprints that let customers count embodied emissions. NVIDIA developer blog

Models

What this company ships, and what each prompt costs

ModelWeightsEnergy / promptWaterTraining CO₂e
Nemotron (Ultra / Super)
49B–253B (Llama-derived)
Open0.30 Wh Estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.0.090 mL Estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.5,000 t Estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.

Where the compute lives

Named sites from company disclosures and press reporting

OperationalConstructionPlanned
  • Santa Clara (Eos / Selene)California, USA · Research clusters; 4,608 H100s in Eos.
  • TSMC fabsHsinchu / Tainan, Taiwan · Where every data-centre GPU is made; 129 Mm³ of water in 2024.
  • TSMC Fab 21Arizona, USA · Blackwell wafers from late 2025.