@FinanceYF5: AMD is trying to bypass NVIDIA’s CUDA moat by pouring in resources. It is committing substantial engineering and compute resources to vLLM. This will benefit the entire ecosystem, including NVIDIA, but the returns for the two companies are not symmetric. Its software stack is still in its early stages, so it is more eager to see inference decoupled from specific hardware.…
Summary
AMD is pouring substantial engineering and compute resources into vLLM to bypass NVIDIA’s CUDA moat, drive the standardization of inference software, and break hardware lock-in—benefiting the entire ecosystem while yielding asymmetric returns for the two companies.
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AMD is trying to bypass NVIDIA’s CUDA moat by pouring resources into the effort.
It is committing substantial engineering and compute resources to vLLM. This will benefit the entire ecosystem, including NVIDIA, but the returns for the two companies are not symmetrical.
Its software stack is still in its early stages, so it has a stronger interest in decoupling inference from specific hardware. It doesn’t need to own the software layer; as long as the software is sufficiently standardized and commoditized, AMD https://t.co/tW9TITxvWm
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@Rick5767: As far as I know, several developers have already broken away from vLLM and llama.cpp to write inference engines from scratch, specifically for AMD GPUs. The gist of it seems to be that when they saw a general-purpose engine only using 200 GB out of 600 GB of GPU memory bandwidth, they thought, 'good lord, what on earth is going on.' With AI-assisted programming going mainstream, a flood of previously impossible projects is about to emerge.
The author notes that developers are already moving away from vLLM and llama.cpp to build custom inference engines from scratch for AMD GPUs, because general-purpose engines are only tapping about 200 GB of a 600 GB memory bandwidth budget. As AI-assisted coding becomes widespread, projects like these will proliferate, ushering in the most egalitarian era in software development.
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