I think a better analogy is firmware binary blobs in the Linux kernel, or VM bytecodes.
The LLM inference engine (architecture implementation) is like a kernel driver that loads a firmware binary blob, or a virtual machine that loads bytecode. The inference engine is open source. The problem is that the weights (firmware blobs, VM bytecodes) are opaque: you don't have the means to reproduce them.
The Linux community has long argued that drivers that load firmware blobs are cheating: they don't count as open source.
Still, the "open source" LLMs are more open than "API-gated" LLMs. It's a step in the right direction, but I hope we don't stop there.
If we're continuing the analogy, the compute required to turn the source into binaries costs millions of dollars. Not a license fee for the compiler, but the actual time on a computer.
Comments
I think a better analogy is firmware binary blobs in the Linux kernel, or VM bytecodes.
The LLM inference engine (architecture implementation) is like a kernel driver that loads a firmware binary blob, or a virtual machine that loads bytecode. The inference engine is open source. The problem is that the weights (firmware blobs, VM bytecodes) are opaque: you don't have the means to reproduce them.
The Linux community has long argued that drivers that load firmware blobs are cheating: they don't count as open source.
Still, the "open source" LLMs are more open than "API-gated" LLMs. It's a step in the right direction, but I hope we don't stop there.
If we're continuing the analogy, the compute required to turn the source into binaries costs millions of dollars. Not a license fee for the compiler, but the actual time on a computer.