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Alternatives exist, especially for mature and simple models. The point isn't that Nvidia has 100% market share, but rather that they command the most lucrative segment and none of these big spenders have found a way to quit their Nvidia addiction, despite concerted efforts to do so.

For instance, we experimented with AWS Inferentia briefly, but the value prop wasn't sufficient even for ~2022 computer vision models.

The calculus is even worse for SOTA LLMs.

The more you need to eke out performance gains and ship quickly, the more you depend on CUDA and the deeper the moat becomes.






llm inference is fine on rocm. llama.cpp and vllm both have very good rocm support.

llm training is also mostly fine. I have not encountered any issues yet.

most of the cuda moat comes from people who are repeating what they heard 5-10 years ago.




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