@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.
Summary
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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Cached at: 10/06/26, 05:53 PM
To my knowledge, several people have already broken away from vLLM and llama.cpp to write inference engines built from scratch specifically for AMD GPUs.
Their reasoning is roughly this: when they see that a general-purpose engine is only using 200GB out of 600GB of available GPU memory bandwidth, they think — what on earth is going on?
With the rise of AI-assisted programming, a flood of things that were previously impossible will now emerge at a staggering pace.
Software is entering the most democratized era in history.
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