How Much Energy Do LLMs Use? A Simple Explanation in Light Bulbs

Large language models (LLMs) like ChatGPT, Gemini, or Llama feel almost weightless when you type a question—but behind the scenes, they run on huge data centers that consume real electricity. In this article, we estimate how much energy an LLM uses and translate it into something intuitive: how many ordinary light bulbs could run, and for how many days, using the same amount of power.

We will look at:

  • How much energy it takes to train a modern LLM
  • How much energy it takes to answer a single query
  • What that means in terms of bulbs and days of lighting
  • How companies are working to reduce AI energy consumption

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The rest of the article is written to answer informational search intent: readers who want a clear, practical explanation of LLM power usage and relatable comparisons.


Visualizing AI Energy Use

Before we dig into numbers, it helps to visualize LLM energy consumption as a flow of power from the data center to something familiar, like a light bulb.

A glowing light bulb symbolizing electricity consumption and energy usage

In the next sections, we will turn technical estimates into bulb-and-days comparisons you can easily remember.


How Much Energy Does an LLM Consume Overall?

A large language model uses energy in two main phases:

  1. Training – teaching the model using massive datasets on powerful GPUs/TPUs in data centers.
  2. Inference – running the trained model to answer user queries.

Both phases can be substantial, but they behave differently:

  • Training is a one‑time (or occasional) huge energy spike.
  • Inference is an ongoing cost that scales with how many people use the model.

Estimates below are based on public research, vendor disclosures, and typical GPU cluster configurations up to late 2024. Exact numbers vary by model, hardware, data center efficiency, and software optimizations, so treat the figures as order‑of‑magnitude approximations, not precise measurements.


Energy Used to Train a Large Language Model

Training a cutting‑edge LLM (tens or hundreds of billions of parameters) typically involves:

  • Thousands to tens of thousands of GPUs or TPUs
  • Running continuously for weeks to months
  • Power per accelerator often between 250–700 watts, plus overhead

Back‑of‑the‑envelope training energy estimate

Let’s pick a concrete (but still simplified) scenario for a modern frontier‑scale LLM:

  • Compute cluster: 10,000 GPUs
  • Average power per GPU (including overhead): 500 W (0.5 kW)
  • Training duration: 30 days of effective full‑load training

Total power draw:

10,000 GPUs × 0.5 kW = 5,000 kW (5 MW)

Energy over 30 days:

5,000 kW × 24 hours/day × 30 days = 3,600,000 kWh (3.6 GWh)

Converting training energy into light bulbs

Suppose we compare this to 10‑watt LED bulbs, a common modern bulb rating.

One 10‑W bulb running for 1 hour uses:

10 W × 1 h = 10 Wh = 0.01 kWh

So with 3,600,000 kWh of energy, you could run:

3,600,000 kWh ÷ 0.01 kWh per bulb‑hour = 360,000,000 bulb‑hours

That is:

  • 1 million bulbs running for 360 hours, or about 15 days, or
  • 100,000 bulbs running for 3,600 hours, or about 150 days (5 months).

In other words, training one very large LLM once can consume roughly as much electricity as keeping a medium city’s worth of LED bulbs lit for weeks or months.

Smaller models use less, but still matter

Not every model is this large. For a mid‑sized LLM trained on, say, 1,000 GPUs for 10 days at 400 W:

1,000 × 0.4 kW × 24 × 10 ≈ 96,000 kWh

With 96,000 kWh, a 10‑W LED bulb could run for:

96,000 kWh ÷ 0.01 kWh per bulb‑hour = 9,600,000 bulb‑hours

That equals:

  • 1,000 bulbs for 9,600 hours (~400 days), or
  • 10,000 bulbs for 960 hours (~40 days).

Energy Used When an LLM Answers a Query (Inference)

Once a model is trained, each interaction (inference) also uses energy. The exact amount depends on:

  • Model size and architecture
  • Prompt length and response length (how many tokens)
  • Hardware (GPU vs CPU vs specialized accelerator)
  • Batching (how many user requests are processed together)
  • Data center efficiency (cooling, power distribution, PUE)

Per‑query electricity cost: What research suggests

Academic and industry estimates up to late 2024 typically place per‑query energy use for large LLMs (e.g., GPT‑3‑class) in the range of roughly:

  • 0.01–0.1 Wh per short query (tens to hundreds of tokens)

For a practical mid‑range estimate, we can use:

~0.05 Wh per typical LLM request

Comparing one LLM query to a light bulb

Again using a 10‑W LED bulb:

A 10‑W bulb using 0.05 Wh corresponds to:

Time = Energy ÷ Power = 0.05 Wh ÷ 10 W = 0.005 hours ≈ 18 seconds

So roughly speaking:

  • Answering one LLM query uses around the same electricity as keeping a 10‑W LED bulb on for about 15–20 seconds.

Scaling up: millions or billions of queries

Now imagine the model serves 1 billion queries in a month at 0.05 Wh each:

1,000,000,000 × 0.05 Wh = 50,000,000 Wh = 50,000 kWh

50,000 kWh for 10‑W bulbs gives:

50,000 kWh ÷ 0.01 kWh per bulb‑hour = 5,000,000 bulb‑hours

That equals:

  • 10,000 bulbs lit for 500 hours (~21 days), or
  • 1,000 bulbs lit for 5,000 hours (~208 days).

Putting It Together: Training vs. Usage in Bulbs and Days

It helps to compare the one‑time training cost with the ongoing usage (inference) cost over the life of the model.

Example: A large‑scale production LLM

Assume:

  • Training energy: 3,600,000 kWh (roughly our earlier frontier‑scale estimate)
  • Queries served over lifetime: 100 billion (over a couple of years)
  • Per‑query energy: 0.03 Wh (assuming better optimizations at scale)

Total inference energy:

100,000,000,000 × 0.03 Wh = 3,000,000,000 Wh = 3,000,000 kWh

That means:

  • Training: ~3.6 million kWh
  • Inference: ~3.0 million kWh

So over its full life, this hypothetical model might split energy consumption roughly 50/50 between training and usage.

Bulb‑and‑days equivalent (10‑W LEDs)

Combined energy: 3,600,000 + 3,000,000 = 6,600,000 kWh.

In bulb‑hours:

6,600,000 kWh ÷ 0.01 kWh per bulb‑hour = 660,000,000 bulb‑hours

This equals:

  • 1 million 10‑W bulbs lit for 660 hours (~27.5 days), or
  • 100,000 bulbs lit for 6,600 hours (~9 months), or
  • 10,000 bulbs lit for 66,000 hours (~7.5 years).

This illustrates why discussions about the energy footprint of AI are becoming important in climate and infrastructure planning.


What Factors Influence LLM Energy Consumption?

Not all LLMs are equal in power usage. Key drivers include:

1. Model size and architecture

  • Larger parameter counts generally mean more compute per token.
  • Newer architectures (e.g., sparsity, mixture‑of‑experts) can reduce average compute while keeping quality high.

2. Hardware efficiency

  • Modern GPUs and TPUs offer more FLOPs per watt than older chips.
  • Dedicated AI accelerators can dramatically improve energy efficiency per query.

3. Data center design

Metrics like PUE (Power Usage Effectiveness) describe how much extra energy is required beyond the IT load for cooling and overhead:

  • A PUE of 1.2 means 20% overhead on top of the hardware power draw.
  • Better cooling (liquid cooling, optimized airflow) and location (cool climates) help reduce total energy.

4. Software and optimization techniques

  • Quantization: using lower‑precision numbers (e.g., 8‑bit) to cut compute per token.
  • Pruning: removing redundant weights.
  • Distillation: training smaller models to mimic larger ones.
  • Caching and batching: serving many users efficiently from shared computations.

5. User behavior and application design

  • Very long prompts and responses require more tokens and more energy.
  • Unnecessary recomputation (e.g., re‑asking the same question repeatedly) increases load.
  • Using smaller, specialized models when possible can substantially lower power usage.

From Energy to Carbon Footprint

Energy consumption is only part of the environmental impact; the other part is how that electricity is generated.

A rough rule of thumb (varies by region and year):

  • Global average grid intensity might be in the ballpark of 0.3–0.5 kg CO₂ per kWh.

Using 0.4 kg CO₂/kWh as an example:

3,600,000 kWh (training) × 0.4 kg CO₂/kWh = 1,440,000 kg CO₂ (1,440 tonnes)

However:

  • If a data center uses mostly renewable energy, its carbon footprint can be dramatically lower.
  • Companies increasingly sign power purchase agreements (PPAs) to source clean energy for AI workloads.

When evaluating AI sustainability, it’s important to consider both how much energy is used and where that energy comes from.


How the Industry Is Reducing LLM Energy Use

Because LLM energy consumption affects costs, reliability, and climate impact, there is strong pressure to improve efficiency. Some key trends:

1. More efficient hardware generations

Each new GPU/TPU generation tends to deliver more operations per watt, meaning:

  • Lower energy cost per token
  • Ability to host more users at the same data center power budget

2. Smarter model design

Techniques like:

  • Mixture‑of‑experts (MoE), where only part of the model is active per token.
  • Hybrid retrieval+LLM setups, where cheaper search or smaller models handle easy queries.
  • Specialized smaller models for narrow tasks, reserving big LLMs for complex questions.

3. Operational best practices

  • Automatically routing traffic to the most efficient region or hardware.
  • Load‑based scaling, turning hardware up or down depending on demand.
  • Using renewable‑rich regions (e.g., hydro, solar, wind) for energy‑intensive training runs.

4. Transparency and reporting

Researchers and AI providers are beginning to:

  • Publish energy and emissions estimates for major models.
  • Offer per‑query CO₂ estimates in developer dashboards.
  • Allow customers to choose “green regions” for their AI workloads.

How to Estimate LLM Energy as Bulbs and Days Yourself

If you want to estimate the electricity usage of an AI application in terms of light bulbs, you can use a simple three‑step method.

Step 1: Estimate total energy in kWh

You need one of:

  • Total data center power (kW or MW) × hours used, or
  • Energy per query (Wh) × number of queries, or
  • Published energy metrics from your cloud provider.

Step 2: Choose a bulb rating

Common choices:

  • 10‑W LED (efficient modern household bulb)
  • 60‑W incandescent (older, less efficient bulb)

Step 3: Convert kWh into bulb‑hours and days

Use:

Bulb‑hours = Total kWh ÷ (Bulb power in kW)

For a 10‑W bulb, power = 0.01 kW. So:

Bulb‑hours = Total kWh ÷ 0.01

To get days per bulb:

Days per bulb = Bulb‑hours ÷ 24

Then you can scale by how many bulbs you want (multiply or divide accordingly).


Accessibility, Performance, and Responsible AI Use

When building AI‑powered websites and applications, it is important not only to consider energy and cost but also accessibility and user experience.

  • Follow WCAG 2.2 guidelines for keyboard navigation, color contrast, and readable text.
  • Use alt text for images so screen readers can describe them.
  • Optimize pages for mobile performance to reduce bandwidth and indirectly reduce energy use on user devices and networks.
  • Avoid unnecessary or repeated LLM calls (for example, caching and summarizing) to keep the energy footprint per user task low.

By designing both the AI and the interface thoughtfully, you can reduce wasted computation while making the experience more inclusive and efficient.


Conclusion: How Many Bulbs for How Many Days?

To summarize the main estimates:

  • Training a frontier‑scale LLM can consume on the order of millions of kWh, enough to power around 100,000–1,000,000 LED bulbs for weeks to months, depending on the scenario.
  • Each individual query uses approximately as much energy as a 10‑W LED bulb running for tens of seconds, though this varies widely by model and setup.
  • Over a model’s lifetime, total energy for all queries can be similar to or even larger than the one‑time training cost.

These numbers show that LLMs are not “free” from an energy perspective—but they are also not wildly out of proportion with other large‑scale digital services such as video streaming or gaming when normalized per user action. The key is to:

  • Build efficient models and infrastructure,
  • Use renewable energy wherever possible, and
  • Design applications that minimize unnecessary computation.

If you develop or operate AI products, it is worth tracking and optimizing your LLM’s energy footprint—both to control costs and to reduce environmental impact, one light bulb‑equivalent at a time.


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