Text / Chat

GLM-5 Environmental Impact

StandardEstimated

First frontier model trained entirely on Huawei Ascend — NVIDIA-free

Architecture
MoE (40B active, trained on Huawei Ascend 910B)
Parameters
744B
Context
128,000 tokens
Provider
Zhipu AI
1.2 Wh
Energy per query
0.66 g
CO₂ per query
4 mL
Water per query
4x more than
vs Google search

Energy per query

1.2 Wh

4x more than a Google search (0.3 Wh)

CO2 per query

0.66 g

China grid (550 gCO₂/kWh)

Water per query

4 mL

~227 queries to fill 1 litre

Processing location

Zhipu AI Cloud (China)

Provider

Zhipu AI

Category

Text / Chat

Grid carbon intensity

550 g CO2/kWh (30% renewable)

How does GLM-5 compare?

Ranked #66 of 152 models by energy per query

0 Wh0.3 Wh0.6 Wh0.9 Wh1.2 WhLLaMA 3.2 1BGemini 1.5 ProGPT-4.1 NanoGLM-5Google search (0.3 Wh)

Detailed Breakdown

Energy Consumption

GLM-5 is notable as the first frontier model trained entirely on Huawei Ascend 910B chips — completely NVIDIA-free. At 744B total parameters with 40B active (MoE), it consumes ~1.2 Wh per query. This represents a significant milestone for Chinese AI independence from Western hardware.

Power Source & Carbon

Runs on Huawei Ascend hardware in Chinese data centres. The Ascend 910B's power efficiency differs from NVIDIA GPUs — exact comparisons are limited by lack of public data.

Water Usage

At ~4.4 mL per query on Chinese infrastructure.

About GLM-5

GLM-5 is an open-source text and chat model from Zhipu AI, released in January 20, 2026, that runs well below the category average for energy consumption at 1.2 Wh per query. Because its weights are publicly available, it can be self-hosted on any infrastructure — meaning its carbon footprint depends entirely on where and how you choose to run it. At 744B parameters, it first frontier model trained entirely on huawei ascend — nvidia-free.

These figures are estimates derived from hardware specifications and API benchmarks — Zhipu AI has not published official energy data for GLM-5. Actual consumption may vary significantly depending on batching, quantisation, and infrastructure optimisations that we cannot observe from outside.

Key Insights

Open-source weights — can be self-hosted on infrastructure you control

Zhipu AI GLM Family

How energy efficiency has evolved across versions.

What does your GLM-5 usage cost the planet?

Use our calculator to estimate your personal environmental footprint based on how often you use GLM-5.

Calculate My Compute

Frequently Asked Questions

How much energy does GLM-5 use per query?

Each GLM-5 query consumes approximately 1.2 Wh of energy. This is 4x more than a traditional Google search (~0.3 Wh).

What is GLM-5's carbon footprint?

Based on the carbon intensity of Zhipu AI Cloud (China), each query produces approximately 0.66 g of CO2. The grid in this region has a carbon intensity of 550 g CO2/kWh with 30% renewable energy.

How much water does GLM-5 use?

Each query consumes approximately 4 mL of water, primarily used for cooling the data centers that process the request.

How does GLM-5 compare to a Google search?

A GLM-5 query uses 4x more than a Google search in terms of energy. A Google search uses approximately 0.3 Wh, while GLM-5 uses 1.2 Wh.

Technical Details

Architecture

MoE (40B active, trained on Huawei Ascend 910B)

Parameters

744B

Context window

128,000 tokens

Release date

2026-01-20

Open source

Yes

Training data cutoff

2025-12