Aptos Will Reduce The Gas Fee Of Dynamic NFT By 10 Times

Key Points:

• Aptos stated that it would enhance the functionality of NFT data and reduce the gas fee by 10 times for dynamic NFT starting in January 2023.

• It will create an advanced gas fee model by the end of the first quarter of 2023, separate storage and execution costs, and deliver gas fees based on demand.

• The team also provides development orientations in stages such as short-term, medium-term, and longer-term.

Aptos announced that in January 2023, it would improve the operation of NFT data and reduce the gas fee of dynamic NFT by 10 times; at the end of the first quarter of 2023, it will design an advanced gas fee model, separate storage, and execution costs, and provide Gas cost driven by demand.

The Aptos team frequently interacts with community builders to support their implementations and collect input on how to make the ecosystem better. The gas schedule has to be improved according to popular demand.

This requirement is in line with a fundamental tenet of Aptos, according to which the network’s demand should heavily influence the costs of using the blockchain.

The team said in a Medium post about the 3-phase plan to provide gas costs on Aptos:

• Early January 2023: Improve operations on NFT data to drop prices by 10x for dynamic NFTs.

• Q1 2023: Build gas-efficient data structures with end-to-end support from guides to indexing.

• Late Q1, early Q2 2023: Devise an advanced gas model that separates storage and execution costs, thus providing execution demand-driven gas costs for execution.

Initially, the team set out with a goal of reducing gas rates by 10 to 100x. To validate the possibility, the team spoke directly with many ecosystem builders including Aries, AUX, Econia, Ferum, Nutrios, Pyth, Souffl3, Switchboard, Topaz, Tsunami, and many more.

In addition, Aptos also provides development orientations in stages such as short-term, medium-term, and longer-term. Including processes to help dynamic NFT gas reduction, as mentioned in this article, gas-efficient data structures, and demand-driven gas costs.

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