GLYNT.AI is Green AI
GLYNT.AI Is the Sustainable Way to Do Sustainability Data
GLYNT.AI’s Few Shot machine learning reduces the energy, water and carbon to do sustainability reporting, using 95% less energy and carbon than LLMs.
HOW IT WORKS
LLMs run on massive models. GLYNT.AI runs on tiny ones
GLYNT.AI’s Few Shot models are small and purpose-built, not massive and general, using a fraction of the compute, energy, and emissions of an LLM.
99.5% accuracy
GLYNT.AI’s specialized models are highly accurate from the start, keeping compute low.
Low-energy use
GLYNT.AI’s AI needs very little compute resources to train models or to process data.
Low-carbon data centers
GLYNT.AI’s cloud computing is in regions with lots of hydro power, further reducing emissions.
BEHIND THE NUMBERS
Full environmental accounting for our green AI
Full environmental accounting for our green AI
GLYNT.AI’s calculation methodology is based on primary data sources and conservative assumptions. Even if we’re 50% wrong, our main conclusion holds: GLYNT.AI’s AI is green AI.
Read more in our guide AI With Lower Emissions
RELATED QUESTIONS
Green AI questions answered
Browse through these FAQs to find answers to commonly raised questions about GLYNT.AI’s sustainable and green AI.
How does GLYNT.AI have a lower carbon footprint than LLMs?
GLYNT.AI uses a Few Shot machine learning system built on thousands of small, specialized models instead of one massive, general-purpose model like an LLM. Because each model is tiny and trains on just 1-3 samples, GLYNT.AI uses 95% less energy, water and carbon per transaction than LLMs use for comparable tasks.
See our White Paper for more details: AI With Lower Emissions
What is Few Shot machine learning, and how is it different from LLMs?
Few Shot machine learning trains individual, task-specific models on only a few samples (as few as 1-3) rather than relying on one huge, general-purpose model trained on massive datasets. GLYNT.AI maintains a library of thousands of these small models, each requiring low compute for both training and inference — unlike LLMs, which depend on large-scale compute and data center infrastructure to run.
How much less energy does GLYNT.AI use compared to large language models?
GLYNT.AI uses less than 5% of the energy LLMs use for the same tasks. Measured per 1,000 rows of data processed, GLYNT.AI’s system consumes about 0.1033 kWh of energy and produces roughly 0.02187 metric tons of CO2e.
How accurate is GLYNT.AI's AI compared to LLMs?
GLYNT.AI’s Few Shot models achieve 99.5% accuracy on sustainability data tasks, compared to less than 65% accuracy for LLMs on the same tasks — even when LLMs use Retrieval Augmented Generation (RAG). That gap means LLMs typically need nearly twice the workload to approach GLYNT.AI’s accuracy, which adds further to their energy and carbon cost.
See our White Paper for more details: AI With Lower Emissions
Does GLYNT.AI use less water than LLMs?
Yes. GLYNT.AI uses less than 5% of the water that LLMs use for comparable tasks, amounting to about 0.0000516 cubic meters (about 52 milliliters) of water per 1,000 rows of data processed.
How does GLYNT.AI reduce emissions from its infrastructure?
Beyond using smaller, more efficient models, GLYNT.AI runs its infrastructure in cloud regions powered largely by hydroelectricity, further reducing emissions.
How should companies report GLYNT.AI's emissions in their sustainability disclosures?
GLYNT.AI recommends that customers report its emissions under Scope 3, Category 1 (purchased goods and services), which includes indirect emissions from vendors. The amount reported is based on the total number of data rows delivered by GLYNT.AI during the reporting period.