Nvidia actively prevents hyperscaler concentration in GPU allocation, which sustains the independent inference provider ecosystem and benefits both Nvidia and end users.
The inference provider layer resists commoditization partly because Nvidia's top priority is avoiding customer concentration — they want many separate GPU allocations across the market, which keeps specialized providers alive and drives innovation in model serving. ✦ AI generated
Alex Atallah · 20VC · 2026-08-10 · original ↗
starts at this moment · 5:55
“A lot of people suggest that that inference provider layer is a commoditizable element or layer that will be removed or see margin reduction competed out over time. What would you say to that theory?”
Why doesn't Google or Amazon or Azure run around and like buy up all the GPUs and take all these inference providers out of business? Well, the people making the GPUs don't want that. Like one of Nvidia's top priorities is not having customer concentration. They want lots of customers to all have like separate like allocations of GPUs. Um they want the the heterogeneity of the market. They want like competition on the compute layer. Um and I and this is good for the ecosystem. Like like users also want this. This like this is it's good for Nvidia and it's good for end users as well. It like allows these inference providers to kind of like come up with new innovations on like how to serve the models better.
verbatim transcript · starts at 5:55
5:55competed out over time. What would you say to that theory? >> Right now we're in a massively supply constrained market where um but and it's likely going to be supply constrained for a while where [snorts] all the inference providers are are short, pretty much constantly short. Uh and and you're like, "Okay, so GPUs are are really really beneficial." And like, "Why doesn't Google or Amazon or Azure
6:24run around and like buy up all the GPUs and take all these inference providers out of business?" Well, the people making the GPUs don't want that. Like one of Nvidia's top priorities is not having customer concentration. They want lots of customers to all have like separate like allocations of GPUs. Um they want the the heterogeneity of the market. They want like competition on the compute layer. Um and I and this is good for the
6:54ecosystem. Like like users also want this. This like this is it's good for Nvidia and it's good for end users as well. It like allows these inference providers to kind of like come up with new innovations on like how to serve the models better. Even a single model like Kimiko 3 um uh like Moonshot just posted a benchmark showing all the inference providers and how well they're serving Kimiko 3
7:19um and uh the they're they're pretty different numbers for like for benchmarks that are really static, that are well known. We post this continuously all the time. We always are like benchmarking all of the models on all of the inference providers, all the open weight providers, and finding really different results con- constantly. The results change over time. Um these models are like very They're very emotional. They're They're
7:49very like They're very like non-deterministic. So, uh um >> I had Lin on my show from Fireworks and she said that, you know, I said about Gavin Baker and a token is a token is what he said. >> Uh-huh. >> And she kind of corrected me that a token is not a token, actually, because one provider can make a token go so much further than another token. It's like,