MISSION
Our industry-first optimiser delivers more capable models faster while saving millions of hours of compute, megawatts, and dollars.
PROBLEM
Arms race to build the biggest LLMs
As the size of Neural Networks increases, so does the time, cost, energy - and data - required to train them. Our industry-first AI-driven optimiser enables you to get much more out of the data you already have. By micro-managing the operational parameters of any neural network, Inephany's technology delivers massive boosts in sample efficiency, leading to substantial performance gains as well as significant reductions in compute. And because our optimiser is itself a Foundation Model, you will be able to fine-tune it to be even more effective on the networks and datasets you care about.
100B
(USD)
AI models by 2027, projected by leaders and experts in the field.
100M
(USD)
or more total training costs for GPT-4 and similar LLMs.
10x
Surge in costs for each new generation of models.
1MW/hour
(PER DAY)
Average energy consumption of a data centre cluster for operating GPUs during training and inference, raising concerns about energy grid sustainability.
700K
(KG CO2)
Carbon emissions produced during a single training run for a large LLM, equivalent to the annual emissions of 100+ average cars.
COMING SOON
A revolutionary foundation model to optimise AI
Inephany's agents can optimise and improve any type of neural network at both training and serving time. This includes the Transformers used for Generative AI, the RNNs used for financial time series modelling, or the CNNs used for object recognition in self-driving cars.
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