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There’s a rather interesting piece of data in the compute space: currently, as much as 30% of GPU compute power in global data centers is not being fully utilized. High-performance GPUs like NVIDIA’s H100 are typically sold at high prices and through long-term contracts, which means small teams and researchers simply can’t afford them. The decentralized compute market Akash Network is specifically designed to solve this problem—enabling GPU providers to monetize idle resources, while consumers can access high-performance compute at lower cost during flexible time windows.
30% of compute capacity is left idle and wasted, while at the same time there are many teams that can’t use the same level of compute due to prices being too high. This isn’t a problem of “not enough compute”—it’s a problem of “poor compute allocation efficiency,” and decentralized networks are exactly what they’re best at solving.
apiarys is an aggregation platform that can directly call various AI models and agents. It charges per usage, and its revenue is settled in $HNY. Much like Akash Network’s logic of “reallocating idle compute,” apiarys also runs AI tasks on real physical GPUs—not concept packaging. It even includes golden quant agents that are already live in production. The project’s provided backtesting data shows daily returns of 1.5%–2% and monthly returns of 30%–40% (depending on market conditions; historical data does not guarantee future performance). The edge comes from real compute running real executions.
The project is currently in an early stage, and both the rules and the product form are continuously being refined. The participation threshold is very low, and $HNY-d6b0 is still an undervalued position.
On one side, 30% of compute is wasted while idling. On the other side, countless teams can’t afford compute. In your view, who will truly solve this kind of “efficiency mismatch” in the future?
There’s a rather interesting piece of data in the compute space: currently, as much as 30% of GPU compute power in global data centers is not being fully utilized. High-performance GPUs like NVIDIA’s H100 are typically sold at high prices and through long-term contracts, which means small teams and researchers simply can’t afford them. The decentralized compute market Akash Network is specifically designed to solve this problem—enabling GPU providers to monetize idle resources, while consumers can access high-performance compute at lower cost during flexible time windows.
30% of compute capacity is left idle and wasted, while at the same time there are many teams that can’t use the same level of compute due to prices being too high. This isn’t a problem of “not enough compute”—it’s a problem of “poor compute allocation efficiency,” and decentralized networks are exactly what they’re best at solving.
apiarys is an aggregation platform that can directly call various AI models and agents. It charges per usage, and its revenue is settled in $HNY. Much like Akash Network’s logic of “reallocating idle compute,” apiarys also runs AI tasks on real physical GPUs—not concept packaging. It even includes golden quant agents that are already live in production. The project’s provided backtesting data shows daily returns of 1.5%–2% and monthly returns of 30%–40% (depending on market conditions; historical data does not guarantee future performance). The edge comes from real compute running real executions.
The project is currently in an early stage, and both the rules and the product form are continuously being refined. The participation threshold is very low, and $HNY-d6b0 is still an undervalued position.
On one side, 30% of compute is wasted while idling. On the other side, countless teams can’t afford compute. In your view, who will truly solve this kind of “efficiency mismatch” in the future?