As I understand, one of Quip’s long-term goals is to route workloads to the most suitable computing resource, whether that’s a CPU, GPU, or a quantum computer.
That made me wonder about complex applications. Imagine an ERP system running a large forecast or optimization with hundreds of smaller tasks. What would decide where each task should run?
Will Quip eventually have an intelligent scheduler/assigner, or perhaps even an AI orchestrator, that can break a large workload into smaller jobs and assign each one to the most suitable computing resource?
I think that orchestration layer could become the same important as the computing power itself.
Interested to hear what the Team and community think about this?
As I understand, one of Quip’s long-term goals is to route workloads to the most suitable computing resource, whether that’s a CPU, GPU, or a quantum computer.
That made me wonder about complex applications. Imagine an ERP system running a large forecast or optimization with hundreds of smaller tasks. What would decide where each task should run?
Will Quip eventually have an intelligent scheduler/assigner, or perhaps even an AI orchestrator, that can break a large workload into smaller jobs and assign each one to the most suitable computing resource?
I think that orchestration layer could become the same important as the computing power itself.
Interested to hear what the Team and community think about this?
This made me think back to the Whitepaper. Quip talks about combining different computing resources, but how they’re coordinated in practice will be really interesting to learn.
The current plan is a decentralized job pool. That means the user controls how people get paid (fastest answer, best answer, best 5 answers, etc). So, an AI orchestrator run by the user can do all of this, but it’s not clear we will build it or someone else will.
