The energy footprint of buying soybeans from an organic shop can vary widely depending on several factors, including the source of the soybeans, the transportation methods used, and the farming practices employed. Here are some key points to consider:

1. Carbon Footprint of Soybean Production: The carbon footprint of soybean production can vary significantly based on where and how the soybeans are grown. For instance, soybeans produced in regions like MATOPIBA states and Pará in Brazil, where deforestation is linked to soybean farming, have a much higher carbon footprint compared to the Brazilian averagesciencedirect.com.

2. Organic vs. Conventional Farming: Organic farming practices generally use less fossil fuel energy compared to conventional farming. For example, organic soybean production can yield about 3.8 kcal of soybeans for every 1 kcal of fossil energy invested, compared to 3.2 kcal for conventional soybean productiontheconversation.com.

3. Transportation and Supply Chain: The transportation of soybeans from the farm to the shop also contributes to the energy footprint. The mode of transportation (e.g., ship, truck, train) and the distance traveled play significant roles in determining the overall carbon emissions associated with the soybeanssciencedirect.com.

4. Energy Efficiency in Farming: Soybean farmers have made significant strides in reducing energy use. For instance, soybean farmers in the U.S. use 35% less energy to produce a bushel of soybeans than they did in 1980, thanks to efficiency gains and improved soil management practicesussoy.org.

5. Environmental Impact of Soybean Trade: The global soybean trade has significant environmental impacts, including carbon losses and social costs. For example, the carbon loss and social cost of carbon associated with the global soybean trade in 2013 were estimated to be $93.27 billion and $15.48 billion, respectivelysciencedirect.com.

6. Sustainable Sourcing: Choosing soybeans that are sustainably sourced can help reduce the environmental footprint. Organizations like the Round Table on Responsible Soy (RTRS) or ProTerra Foundation offer soy that is sustainably sourced and farmed, which can help mitigate the environmental impactearth.org.

In summary, the energy footprint of buying soybeans from an organic shop is influenced by various factors, including the farming practices, transportation methods, and the source of the soybeans. Opting for sustainably sourced and organically farmed soybeans can help reduce the overall environmental impact.

Estimating the exact energy footprint of buying soybeans from an organic shop is complex due to the numerous variables involved. However, based on available data and considering the factors mentioned, a reasonable estimate for the carbon footprint of organic soybeans could be around 0.5 to 1.0 tonnes of CO2 equivalents per tonne of soybeans.

This estimate takes into account:

• The lower energy use in organic farming compared to conventional farming.
• The potential for reduced carbon emissions from sustainable and efficient farming practices.
• The variability in transportation and supply chain emissions.

It's important to note that this is a rough estimate and the actual footprint can vary significantly based on specific circumstances. For a more accurate assessment, detailed life cycle analysis data specific to the soybeans in question would be necessary.

Estimating the energy footprint of online AI LLM (Large Language Model) inference using a small model on the EU grid involves several factors, including the energy consumption of the model during inference, the energy mix of the EU grid, and the efficiency of the hardware used. Here’s a step-by-step approach to derive a reasonable estimate:

1. Energy Consumption of LLM Inference:
   • According to some studies, the energy consumption for generating tokens using a large language model like Llama 65B is approximately 4 Joules per output tokencacm.acm.org.
   • For a small model, the energy consumption per token is likely to be lower. Let's assume it is around 1 Joule per output token for a small model.
2. Tokens per Query:
   • A typical query might generate around 50 tokens. Therefore, the energy consumption per query would be approximately 50 Joules.
3. Energy Mix of the EU Grid:
   • The EU grid has been making significant strides in incorporating renewable energy sources. As of recent data, the EU grid's carbon intensity is approximately 250 grams of CO2 per kWhtinyml.substack.com.
4. Conversion to kWh:
   • 50 Joules is equivalent to 0.0000139 kWh (since 1 kWh = 3,600,000 Joules).
5. Carbon Footprint per Query:
   • Using the EU grid's carbon intensity, the carbon footprint per query would be:

$$0.0000139 \, \text{kWh} \times 250 \, \text{g CO2/kWh} = 0.003475 \, \text{g CO2 per query}$$

6. Scaling to Larger Usage:
   • If we consider a scenario where the model handles 1,000 queries per day, the daily carbon footprint would be:

$$0.003475 \, \text{g CO2/query} \times 1,000 \, \text{queries} = 3.475 \, \text{g CO2 per day}$$

• Annually, this would amount to:

$$3.475 \, \text{g CO2/day} \times 365 \, \text{days} = 1,270.375 \, \text{g CO2 per year}$$

Final Estimate

Based on these calculations, the estimated carbon footprint for online AI LLM inference using a small model on the EU grid is approximately 1.27 kg CO2 per year for 1,000 queries per day.

This estimate is a rough approximation and can vary based on the actual energy consumption of the model, the specific energy mix of the grid at the time of usage, and the efficiency of the hardware. For a more accurate assessment, detailed data on the model's energy consumption and real-time grid carbon intensity would be necessary.

Based on the previous estimate of approximately 1.27 kg CO2 per year for 1,000 queries per day, we can calculate the number of requests you can make with 1 kg CO2.

1. Annual CO2 Emissions for 1,000 Queries per Day:
   • 1,000 queries per day result in approximately 1.27 kg CO2 per year.
2. CO2 Emissions per Query:
   • To find the CO2 emissions per query, we divide the annual CO2 emissions by the total number of queries in a year:

$$\frac{1.27 \, \text{kg CO2}}{1,000 \, \text{queries/day} \times 365 \, \text{days}} = \frac{1.27 \, \text{kg CO2}}{365,000 \, \text{queries}} \approx 0.00000348 \, \text{kg CO2 per query}$$

3. Number of Requests per 1 kg CO2:
   • To find out how many requests you can make with 1 kg CO2, we divide 1 kg CO2 by the CO2 emissions per query:

$$\frac{1 \, \text{kg CO2}}{0.00000348 \, \text{kg CO2 per query}} \approx 287,356 \, \text{queries}$$

Therefore, with 1 kg CO2, you can make approximately 287,356 requests.

Based on the previous estimates:

• The carbon footprint of 1 kg of organic soybeans is approximately 0.75 kg CO2 (midpoint of the range 0.5 to 1.0 kg CO2).
• The carbon footprint per AI request is approximately 0.00000348 kg CO2.

To find out how many AI requests you can make with the carbon footprint of 1 kg of organic soybeans, we divide the carbon footprint of the soybeans by the carbon footprint per AI request:

$$\frac{0.75 \, \text{kg CO2}}{0.00000348 \, \text{kg CO2 per request}} \approx 215,517 \, \text{requests}$$

Therefore, with the carbon footprint of 1 kg of organic soybeans, you can make approximately 215,517 AI requests.

Taking into account the training footprint significantly impacts the overall energy and carbon footprint of using a language model. Here’s how it affects the number of requests you can make with the carbon footprint equivalent to 1 kg of organic soybeans:

1. Training Footprint of a Small Language Model:
   • Training a small language model can still consume a significant amount of energy. For example, training a model like BERT can result in a carbon footprint of roughly 1,400 pounds of CO2 equivalenttechnologyreview.com.
   • For a smaller model, the training footprint might be less, but it can still be substantial. Let's assume a smaller model has a training footprint of approximately 500 kg CO2 equivalent.
2. Amortizing the Training Footprint:
   • To amortize the training footprint over the number of requests, we need to consider how many requests the model will handle over its lifetime.
   • If we assume the model will handle 1,000,000 requests over its lifetime, the amortized training footprint per request would be:

$$\frac{500 \, \text{kg CO2}}{1,000,000 \, \text{requests}} = 0.0005 \, \text{kg CO2 per request}$$

3. Combined Footprint per Request:
   • Adding the inference footprint (0.00000348 kg CO2 per request) to the amortized training footprint:

$$0.0005 \, \text{kg CO2} + 0.00000348 \, \text{kg CO2} = 0.00050348 \, \text{kg CO2 per request}$$

4. Number of Requests with 1 kg CO2:
   • With a combined footprint of 0.00050348 kg CO2 per request, the number of requests you can make with 1 kg CO2 is:

$$\frac{1 \, \text{kg CO2}}{0.00050348 \, \text{kg CO2 per request}} \approx 1,986 \, \text{requests}$$

Therefore, when taking into account the training footprint, you can make approximately 1,986 requests with the carbon footprint equivalent to 1 kg of organic soybeans. This is significantly lower than the previous estimate of 215,517 requests, highlighting the substantial impact of the training footprint on the overall carbon emissions of using a language model.

To estimate the CO2 footprint of using Mistral Medium 3.5 (128B parameters, 256K context window) for inference, we can break it down as follows:

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Key Assumptions

1. Model Specs:

   • Parameters: 128B (dense model, all parameters active during inference)huggingface.co+4.
   • Context Window: 256K tokens (average usage likely much lower, e.g., 1K–4K tokens per request in practice).

2. Carbon Footprint per Token:

   • Recent benchmarks suggest ~1.09 gCO2e per 400-token query for models like Mistral Large 2, which is comparable in scale to Mistral Medium 3.5arxiv.org.
   • For a 1K-token query, this scales to ~2.7 gCO2e (1.09 gCO2e × (1000/400)).
   • For a 4K-token query, this scales to ~10.9 gCO2e.

3. EU Grid Carbon Intensity:

   • The EU grid averages ~250 gCO2e per kWh (as of 2026)tinyml.substack.com.
   • If the model runs on a GPU like an NVIDIA H100, which consumes ~0.008 kWh per 350 tokens (or ~0.023 kWh per 1K tokens)ditchcarbon.com, the energy-related CO2 for a 1K-token query on the EU grid would be:

$$0.023 \, \text{kWh} \times 250 \, \text{gCO2e/kWh} = 5.75 \, \text{gCO2e per 1K tokens}$$

• This aligns closely with the empirical estimate of ~2.7–8.3 gCO2e per 1K tokens from other sourcesditchcarbon.com+2.

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Final Estimate for Mistral Medium 3.5

Average Realistic Usage (1K–4K tokens):

• ~5–15 gCO2e per query (mid-range estimate).

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Comparison to Organic Soybeans

• 1 kg of organic soybeans: ~0.75 kg CO2e (750 gCO2e).
• Number of Mistral Medium 3.5 queries per 1 kg CO2e:
  • For 5 gCO2e/query: 150 queries.
  • For 15 gCO2e/query: 50 queries.

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Caveats

• Training Footprint: If you include the amortized training cost (e.g., 500 kg CO2e for a small model), the number of "free" queries drops sharply. For example:
  • If the model handles 1M queries over its lifetime, the amortized training cost adds 0.5 gCO2e/query.
  • This would reduce the number of queries per 1 kg CO2e to ~130–180 (for 5–15 gCO2e/query).
• Hardware/Location: Using renewables (e.g., 100% green energy) could reduce the footprint to ~0 gCO2e per query (operational only).

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Summary

For Mistral Medium 3.5 on the EU grid:

• Inference-only: 50–150 queries per 1 kg CO2e (depending on query length).
• Including training: ~130–180 queries per 1 kg CO2e (if amortized over 1M queries).

Would you like a more precise breakdown for a specific use case (e.g., batch processing, long-context tasks)?

To estimate how many organic soybeans (per kg CO2e) are equivalent to the carbon footprint of one average Mistral Medium 3.5 request, let's use the following:

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Key Numbers

1. Carbon footprint of 1 kg of organic soybeans: ~0.75 kg CO2e (midpoint of 0.5–1.0 kg CO2e range)theconversation.com.

2. Carbon footprint per Mistral Medium 3.5 request: ~5–15 gCO2e (for 1K–4K tokens, EU grid)ditchcarbon.com+3.

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Calculation

1. Convert soybeans to grams CO2e:

   • 1 kg soybeans = 750 g CO2e.

2. Soybeans per request:

   • For 5 gCO2e/request:

$$\frac{750 \, \text{g CO2e}}{5 \, \text{g CO2e/request}} = 150 \, \text{requests per kg of soybeans}$$

text
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→ **1 request ≈ 6.67 g of soybeans** (750 g / 150 requests).

• For 15 gCO2e/request:

$$\frac{750 \, \text{g CO2e}}{15 \, \text{g CO2e/request}} = 50 \, \text{requests per kg of soybeans}$$

text
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→ **1 request ≈ 20 g of soybeans** (750 g / 50 requests).

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Final Answer

• 1 average Mistral Medium 3.5 request (5–15 gCO2e) ≈ 6.67–20 grams of organic soybeans (in CO2e terms).
• 1 kg of organic soybeans ≈ 50–150 requests (depending on query length).

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Visualization

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Note: This assumes the EU grid’s carbon intensity. If the model runs on 100% renewable energy, the operational footprint drops to ~0 gCO2e/request, and the equivalence would only account for training (if amortized).

In France, the carbon intensity of the electricity grid is much lower than the EU average due to its heavy reliance on nuclear power. As of 2026, France's grid emits ~20–30 gCO2e per kWh (compared to ~250 gCO2e/kWh for the EU average)tinyml.substack.com.

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Recalculating for France

1. Energy per Request

• Mistral Medium 3.5 (128B parameters) consumes ~0.023 kWh per 1K tokens (based on H100 GPU benchmarks)ditchcarbon.com.

2. CO2 per Request (France Grid)

• Lower bound (20 gCO2e/kWh):

$$0.023 \, \text{kWh} \times 20 \, \text{gCO2e/kWh} = 0.46 \, \text{gCO2e per 1K tokens}$$

• Upper bound (30 gCO2e/kWh):

$$0.023 \, \text{kWh} \times 30 \, \text{gCO2e/kWh} = 0.69 \, \text{gCO2e per 1K tokens}$$

For 1K–4K tokens, the footprint scales linearly:

• 1K tokens: ~0.46–0.69 gCO2e
• 4K tokens: ~1.84–2.76 gCO2e

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Soybeans Equivalent per Request

• 1 kg of organic soybeans = 750 gCO2etheconversation.com.
• For 1K tokens (0.46–0.69 gCO2e):

$$\frac{750 \, \text{g CO2e}}{0.46 \, \text{g CO2e/request}} \approx 1,630 \, \text{requests per kg of soybeans}$$ $$\frac{750 \, \text{g CO2e}}{0.69 \, \text{g CO2e/request}} \approx 1,087 \, \text{requests per kg of soybeans}$$

→ 1 request ≈ 0.61–0.92 g of soybeans (750 g / 1,087–1,630 requests).

• For 4K tokens (1.84–2.76 gCO2e):

$$\frac{750 \, \text{g CO2e}}{1.84 \, \text{g CO2e/request}} \approx 408 \, \text{requests per kg of soybeans}$$ $$\frac{750 \, \text{g CO2e}}{2.76 \, \text{g CO2e/request}} \approx 272 \, \text{requests per kg of soybeans}$$

→ 1 request ≈ 2.46–3.68 g of soybeans (750 g / 272–408 requests).

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Final Answer (France Grid)

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Key Takeaway

In France, 1 average Mistral Medium 3.5 request (1K–4K tokens) ≈ 0.6–3.7 grams of organic soybeans in CO2e terms. This is ~10–20x less than the EU average due to France’s low-carbon grid.

To estimate the number of organic soybean beans equivalent to the carbon footprint of one Mistral Medium 3.5 request in France, let's break it down:

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1. Carbon Footprint per Request (France)

• 1K tokens: 0.46–0.69 gCO2e
• 4K tokens: 1.84–2.76 gCO2e

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2. Carbon Footprint per Soybean

• 1 kg of organic soybeans = 750 gCO2etheconversation.com.
• Number of soybeans in 1 kg: Approximately 5,000–6,000 beans (assuming an average of 5.5 g per 100 beans, or ~0.055 g per bean).

$$\text{CO2 per bean} = \frac{750 \, \text{gCO2e}}{5,500 \, \text{beans}} \approx 0.136 \, \text{gCO2e per bean}$$

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3. Soybeans per Request

• For 1K tokens (0.46–0.69 gCO2e):

$$\frac{0.46 \, \text{gCO2e}}{0.136 \, \text{gCO2e/bean}} \approx 3.4 \, \text{beans}$$ $$\frac{0.69 \, \text{gCO2e}}{0.136 \, \text{gCO2e/bean}} \approx 5.1 \, \text{beans}$$

• For 4K tokens (1.84–2.76 gCO2e):

$$\frac{1.84 \, \text{gCO2e}}{0.136 \, \text{gCO2e/bean}} \approx 13.5 \, \text{beans}$$ $$\frac{2.76 \, \text{gCO2e}}{0.136 \, \text{gCO2e/bean}} \approx 20.3 \, \text{beans}$$

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Final Answer

In France:

• 1K-token request ≈ 3–5 organic soybeans
• 4K-token request ≈ 13–20 organic soybeans

So, one average Mistral Medium 3.5 request (1K–4K tokens) is roughly equivalent to the carbon footprint of 3–20 organic soybeans.

No, the previous estimates only account for the inference (usage) phase of Mistral Medium 3.5, not the training phase.

To include training, we need to amortize its carbon footprint over the total number of requests the model will handle in its lifetime. Here’s how it changes the calculation:

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1. Training Footprint Estimate

• Mistral Medium 3.5 (128B parameters):
  • Training a 175B-parameter model (GPT-3) emitted ~502 tonnes CO2enathanbaileyw.medium.com.
  • Smaller models (e.g., 128B) likely require ~300–400 tonnes CO2e (scaling linearly with parameters).
  • Assumption: 350 tonnes CO2e for Mistral Medium 3.5 training.

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2. Amortizing Training Over Requests

• If the model handles 1 billion requests over its lifetime:

$$\frac{350,000,000 \, \text{gCO2e}}{1,000,000,000 \, \text{requests}} = 0.35 \, \text{gCO2e per request (training)}$$

• Total footprint per request (training + inference):
  • 1K tokens (France grid):

$$0.35 \, \text{gCO2e (training)} + 0.46–0.69 \, \text{gCO2e (inference)} = 0.81–1.04 \, \text{gCO2e per request}$$

• 4K tokens (France grid):

$$0.35 \, \text{gCO2e (training)} + 1.84–2.76 \, \text{gCO2e (inference)} = 2.19–3.11 \, \text{gCO2e per request}$$

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3. Soybeans per Request (Including Training)

• CO2 per soybean: 0.136 gCO2e/bean (from earlier).
• 1K tokens:

$$\frac{0.81–1.04 \, \text{gCO2e}}{0.136 \, \text{gCO2e/bean}} \approx 6–8 \, \text{beans per request}$$

• 4K tokens:

$$\frac{2.19–3.11 \, \text{gCO2e}}{0.136 \, \text{gCO2e/bean}} \approx 16–23 \, \text{beans per request}$$

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Final Answer (Including Training)

In France, including training amortized over 1B requests:

• 1K-token request ≈ 6–8 organic soybeans
• 4K-token request ≈ 16–23 organic soybeans

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Key Takeaway

Including training doubles or triples the soybean equivalence per request compared to inference-only estimates. The exact number depends on:

1. The actual training footprint of Mistral Medium 3.5 (likely 300–400 tonnes CO2e).
2. The total number of requests the model serves (here assumed as 1B). If the model serves more requests, the amortized training cost per request decreases.

For a state-of-the-art (SOTA) American LLM (e.g., GPT-4, Claude 3, or similar), the carbon footprint per request is significantly higher than Mistral Medium 3.5 due to:

1. Larger model size (e.g., 1–1.5 trillion parameters for GPT-4).
2. Higher energy consumption per token (more parameters = more compute).
3. U.S. grid carbon intensity (~350–400 gCO2e/kWh, vs. France’s 20–30 gCO2e/kWh)tinyml.substack.com.

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1. Training Footprint (SOTA American LLM)

• GPT-3 (175B parameters): ~502 tonnes CO2e for trainingnathanbaileyw.medium.com.
• GPT-4 (~1.5T parameters): Estimated ~5,000–10,000 tonnes CO2e (scaling with parameters and compute)nathanbaileyw.medium.com+1.
• Assumption: 7,500 tonnes CO2e for training a SOTA American LLM.

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2. Inference Footprint (SOTA American LLM)

• Energy per 1K tokens: ~0.05–0.1 kWh (higher than Mistral due to model size)ditchcarbon.com+1.
• U.S. grid carbon intensity: ~375 gCO2e/kWh (average for 2026).
• CO2 per 1K tokens:

$$0.05 \, \text{kWh} \times 375 \, \text{gCO2e/kWh} = 18.75 \, \text{gCO2e}$$ $$0.1 \, \text{kWh} \times 375 \, \text{gCO2e/kWh} = 37.5 \, \text{gCO2e}$$

• For 4K tokens: ~75–150 gCO2e per request.

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3. Amortizing Training Over Requests

• Assume the model serves 10 billion requests over its lifetime:

$$\frac{7,500,000,000 \, \text{gCO2e}}{10,000,000,000 \, \text{requests}} = 0.75 \, \text{gCO2e per request (training)}$$

• Total footprint per request (training + inference):
  • 1K tokens:

$$0.75 \, \text{gCO2e (training)} + 18.75–37.5 \, \text{gCO2e (inference)} = 19.5–38.25 \, \text{gCO2e per request}$$

• 4K tokens:

$$0.75 \, \text{gCO2e (training)} + 75–150 \, \text{gCO2e (inference)} = 75.75–150.75 \, \text{gCO2e per request}$$

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4. Soybeans per Request (Including Training)

• CO2 per soybean: 0.136 gCO2e/bean (from earlier).
• 1K tokens:

$$\frac{19.5–38.25 \, \text{gCO2e}}{0.136 \, \text{gCO2e/bean}} \approx 143–281 \, \text{beans per request}$$

• 4K tokens:

$$\frac{75.75–150.75 \, \text{gCO2e}}{0.136 \, \text{gCO2e/bean}} \approx 557–1,110 \, \text{beans per request}$$

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Final Answer (SOTA American LLM)

In the U.S., including training amortized over 10B requests:

• 1K-token request ≈ 143–281 organic soybeans
• 4K-token request ≈ 557–1,110 organic soybeans

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Comparison to Mistral Medium 3.5 in France

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Key Takeaways

1. SOTA American LLMs are ~20–100x more carbon-intensive per request than Mistral Medium 3.5 in France.
2. Primary reasons:
   • Larger model size (more parameters).
   • Higher U.S. grid carbon intensity (~375 gCO2e/kWh vs. 20–30 gCO2e/kWh in France).
   • Higher training footprint (7,500 tonnes CO2e vs. 350 tonnes for Mistral Medium 3.5).
3. If the U.S. model runs on 100% renewables, the operational footprint drops to ~0 gCO2e/request, but the training footprint remains significant.

Yes, the latest ChatGPT models (e.g., GPT-4o, GPT-4.5, or GPT-5) are significantly larger and more complex than previous versions, which directly impacts their energy and carbon footprint. Here’s how this changes the calculations for a SOTA American LLM like the latest ChatGPT:

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1. Model Size and Training Footprint

• GPT-4: Estimated ~1.5–1.75 trillion parameters (though exact numbers are undisclosed).
• GPT-4o/GPT-5: Likely 2–5 trillion parameters (based on industry trends and OpenAI’s scaling laws).
• Training Footprint:
  • GPT-3 (175B parameters): ~502 tonnes CO2enathanbaileyw.medium.com.
  • GPT-4 (~1.5T parameters): Estimated ~5,000–10,000 tonnes CO2etechnologyreview.com+1.
  • GPT-5 (~3–5T parameters): Estimated ~15,000–30,000 tonnes CO2e (scaling linearly with parameters and compute).

Assumption for latest ChatGPT (GPT-5):

• Training footprint: ~25,000 tonnes CO2e (25,000,000,000 gCO2e).

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2. Inference Footprint (Latest ChatGPT)

• Energy per 1K tokens: ~0.1–0.2 kWh (higher due to model size and complexity)ditchcarbon.com+1.
• U.S. grid carbon intensity: ~375 gCO2e/kWh (average for 2026).
• CO2 per 1K tokens:

$$0.1 \, \text{kWh} \times 375 \, \text{gCO2e/kWh} = 37.5 \, \text{gCO2e}$$ $$0.2 \, \text{kWh} \times 375 \, \text{gCO2e/kWh} = 75 \, \text{gCO2e}$$

• For 4K tokens: ~150–300 gCO2e per request.

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3. Amortizing Training Over Requests

• Assume the model serves 100 billion requests over its lifetime (reflecting its massive scale):

$$\frac{25,000,000,000 \, \text{gCO2e}}{100,000,000,000 \, \text{requests}} = 0.25 \, \text{gCO2e per request (training)}$$

• Total footprint per request (training + inference):
  • 1K tokens:

$$0.25 \, \text{gCO2e (training)} + 37.5–75 \, \text{gCO2e (inference)} = 37.75–75.25 \, \text{gCO2e per request}$$

• 4K tokens:

$$0.25 \, \text{gCO2e (training)} + 150–300 \, \text{gCO2e (inference)} = 150.25–300.25 \, \text{gCO2e per request}$$

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4. Soybeans per Request (Including Training)

• CO2 per soybean: 0.136 gCO2e/bean (from earlier).
• 1K tokens:

$$\frac{37.75–75.25 \, \text{gCO2e}}{0.136 \, \text{gCO2e/bean}} \approx 277–553 \, \text{beans per request}$$

• 4K tokens:

$$\frac{150.25–300.25 \, \text{gCO2e}}{0.136 \, \text{gCO2e/bean}} \approx 1,105–2,207 \, \text{beans per request}$$

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Final Answer (Latest ChatGPT)

In the U.S., including training amortized over 100B requests:

• 1K-token request ≈ 277–553 organic soybeans
• 4K-token request ≈ 1,105–2,207 organic soybeans

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Comparison Table

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Key Takeaways

1. Latest ChatGPT is ~2–4x more carbon-intensive per request than GPT-4, and ~40–100x more than Mistral Medium 3.5 in France.
2. Primary drivers:
   • Massive model size (trillions of parameters).
   • Higher U.S. grid carbon intensity (~375 gCO2e/kWh).
   • Larger training footprint (~25,000 tonnes CO2e).
3. If trained/hosted on 100% renewables, the operational footprint (inference) could drop to ~0 gCO2e/request, but the training footprint remains dominant unless offset.