# Show HN: Estimate your AI CO2 footprint

> Source: <https://llmfootprint.fyi/>
> Published: 2026-09-09 05:02:44+00:00

# What’s your *AI footprint?*

Estimate the energy and emissions behind your AI use. No token counts? Start with an example.

## Your usage

K = thousand · M = million · B = billion · T = trillion

Examples replace all settings; energy estimates are modeled.

## Advanced settings

## Your estimated footprint

Enter token usage to calculate.

**Energy breakdown**

**—** Cached input

**—** Output

**—**

## In everyday terms

Select a comparison for its assumptions and source.

Comparisons are alternative yardsticks, not amounts to add together. Energy-only benchmarks are separate from emissions.

## Methodology & benchmark assumptions

**Example energy estimates are modeled, not measured.**
                The chat and agent assistant presets use illustrative token
                counts, not measured averages. “This page” uses a rounded
                estimate of development usage, not recorded token totals. “Major
                project” uses the reported token totals from
                [Anthropic’s compiler experiment](https://www.anthropic.com/engineering/building-c-compiler)
                —2B input and 140M output tokens across nearly 2,000 sessions.
                That does not establish a typical feature size, cache rate, or
                input/output ratio.
                [Claude Code’s usage documentation](https://code.claude.com/docs/en/costs)
                separates fresh input, cache reads, and cache writes; include
                all three in this calculator’s input total. Example cache rates
                and model classes are assumptions, not measured characteristics
                of those workloads.
              

                “Agent team project” uses
                [Bun’s reported pre-merge Zig-to-Rust rewrite usage](https://bun.com/blog/bun-in-rust): 5.9B uncached input + 72B cached input = 77.9B total input,
                and 690M output. Its 92.4% cache share is rounded to 92% to
                match the whole-percent control. Both sourced projects are
                exceptional examples, not typical workloads; their reported
                token totals do not validate this calculator’s energy
                coefficients.
              

The estimator uses three contemporary public model proxies on modern NVIDIA Blackwell hardware. Its core equation is:

```
E = fresh_input × Jfresh + cached_input × Jcache + output ×
                  Joutput
```

Current per-token coefficients are modeled from public AgentX benchmarks on B300 hardware. The model assumes cache hits use ~56% of fresh-input energy and output costs ~10× per token.

**Flash** Qwen3.8-Flash-Next · 176B total / 6B active · B300-class AgentX proxy

**Frontier** DeepSeek V4 Pro · 1.6T total / 49B active · B300 AgentX proxy

**SOTA** Kimi K3 · 2.8T total / ~104B active · B300 AgentX proxy

**Serving profile matters.** Efficient serving
                batches more work and reduces energy per token; low-latency
                serving sacrifices utilization. The displayed range reflects
                different serving profiles, not a statistical confidence
                interval. Architecture, quantization, context length, cache
                placement, hardware generation, speculative decoding, and
                datacenter routing can move the true value outside it.
              

**Carbon is calculated separately:**
`CO₂e = kWh × grid intensity`. Changing the grid does
                not change estimated inference energy.
              

**Grid presets are scenarios, not routing claims.**
                Presets use EPA eGRID2023 revision 2 annual total-output CO₂e
                rates, not marginal or consumption-based emissions. These
                generation averages do not adjust for electricity imports,
                transmission losses, or upstream fuel emissions. West Virginia
                is a coal-heavy benchmark; selecting it does not imply that a
                request was served there. Dedicated generation or contractual
                clean-energy purchases can differ from these averages.
              

**Benchmark/source links:**
[Qwen3.8 / InferenceX](https://inferencex.semianalysis.com/model/qwen-3-8-flash-next)
                ·
                [DeepSeek V4 Pro / InferenceX](https://inferencex.semianalysis.com/compare/deepseek-v4-b300-vs-gb300)
                ·
                [Kimi K3 / InferenceX](https://inferencex.semianalysis.com/run/kimi-k3-on-b300)
                ·
                [EPA eGRID2023 tables (XLSX)](https://www.epa.gov/system/files/documents/2025-06/summary_tables_rev2.xlsx).