> 17.7 per cent of GPUs allocated to serve only 1.35 per cent of requests in Alibaba Cloud’s marketplace, the researchers found
Instead of 1192 GPUs they now use 213 for serving those requests.
I guess I’d assumed this sort of thing would be allocated dynamically. Of course, there’s a benefit to minimizing the number of times you load a model. But surely if a GPU+model is idle for more than a couple minutes it could be freed?
(I’m not an AI guy, though—actually I’m used to asking SLURM for new nodes with every run I do!)
If you're using an efficient inference engine like VLLM, you're adding compilation into the mix, and not all of that is fully cached yet.
If that kind of latency isn't acceptable to you, you have to keep the models loaded.
This (along with batching) is why large local models are a dumb and wasteful idea if you're not serving them at enterprise scale.
Local models are never a dumb idea. The only time it's dumb to use them in an enterprise is if the infra is Mac Studio with M3 Ultra because pp time is terrible.