Our goal is to make VOLM the world's inference engine, really push the capabilities on the open source front,

and then build the universal inference layer.

That means we'll have the runtime to power any new model on new hardware for new application,

be able to tailor that to extreme efficiency and support all the AI workbook going forward.

I fundamentally believe that open source,

especially how VLM itself is structured,

is critical to the AI infrastructure in the world.

And what we want to do with Infraq

is to support, maintain, steward, and push forward the open source ecosystem.

It is only that VOLM win.

VLM becomes a standard and VLM help everybody to achieve what they need to do.

Then our company in the sense have the right meaning and to be able to support everybody around it.

What if the hardest problem in artificial intelligence isn't training smarter models,

but simply keeping them running? For most of the history of computing, once the system was

built, the hard part was over.

He wrote the program, pressed run, and the machine behaved predictably.

Even early machine learning followed that pattern.

Inputs were standardized, workloads were regular, the computer did its job and stopped.

Large language models quietly broke that assumption.

Every request is different.

Prombs can be a sentence or an entire archive.

Outputs can end instantly or stretch on indefinitely.

...