Yes it's 100% prioritization. Through that it's also likely running on more GPUs at once but that's an artifact of prioritization at the datacenter level. Any task coming into an AI datacenter atm is split into fairly fined grained chunks of work and added to queues to be processed.

When you add a job with high priority all those chunks will be processed off the queue first by each and every GPU that frees up. It probably leads to more parallelism but... it's the prioritization that led to this happening. It's better to think of this as prioritization of your job leading to the perf improvement.

Here's a good blog for anyone interested which talks about prioritization and job scheduling. It's not quite at the datacenter level but the concepts are the same. Basically everything is thought of as a pipeline. All training jobs are low pri (they take months to complete in any case), customer requests are mid pri and then there's options for high pri. Everything in an AI datacenter is thought of in terms of 'flow'. Are there any bottlenecks? Are the pipelines always full and the expensive hardware always 100% utilized? Are the queues backlogs big enough to ensure full utilization at every stage?

https://www.aleksagordic.com/blog/vllm

Until everyone buys it. Like fast pass at an amusement park where the fast line is still two hours long