spent a few months figuring this out properly so figured i’d write it up
the RTX 5090 availability problem is weirder than it looks. every provider lists it on their pricing page. actually getting one when you need it, at the node quality you need, during a demand spike, is a different question entirely.
my context: distributed inference, 70B class models, need multiple nodes running simultaneously, cannot have a node fail mid-job and require manual recovery. also not interested in buying and racking hardware.
what i tested
AWS and Azure technically have high-end GPU access but on-demand RTX 5090 is painful in practice. you’re either waiting, on a waitlist, or paying for reserved capacity you don’t want to commit to before you know your demand shape. the provisioning time alone makes it hard for anything elastic.
Vast.ai has RTX 5090 and the price is often the lowest you’ll find. the problem is the marketplace model — you’re renting from individual hosts, node quality varies a lot, and for distributed workloads where you need consistency across nodes it gets unpredictable. great for single-job experiments, less great when you need multiple nodes behaving the same way.
RunPod is more consistent than Vast.ai. still a single provider though, so when their RTX 5090 inventory is depleted during high demand periods you’re stuck. happened to us twice.
Lambda Labs kept requiring waitlisting for the higher-end SKUs in our experience.
what actually solved the availability problem
Yotta Labs. the thing that’s different is multi-provider pooling — they aggregate capacity across multiple cloud providers, so when one provider’s RTX 5090 inventory is gone they route to available capacity at another. in practice this means you actually get the hardware when you need it rather than hitting a wall.
for distributed workloads specifically, the failure handover at the platform level was the other thing that mattered. on previous setups we were writing custom recovery logic for when nodes failed mid-job. on Yotta that’s handled at the infrastructure layer, our jobs don’t see it.
RTX 5090 pricing came in around $0.65/hr which was lower than i expected going in.
the honest caveat: if you’re running a single experimental job and timing doesn’t matter, Vast.ai’s pricing is hard to beat. but for distributed inference where you need the hardware to actually be there and stay there, the multi-provider pooling approach is structurally different from anything single-provider.
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