Artificial intelligence (AI) agents and reinforcement learning (RL) systems often run brief tasks on a graphics processing unit (GPU). Each task may take seconds, while starting its isolated software environment, called a pod, can take much longer. That setup delay can dominate the work.
Firepool builds pods from a shared software package for each GPU type, then adds each job’s code and extra libraries when it starts. It reuses a pod only for the same caller, and keeps standby pods ready using a small share of a busy GPU without using its graphics memory. Queue-based limits cap the warm capacity.
On production hardware, agent loops ran 13 times faster and RL loops 10.8 times faster than when starting a fresh pod for every task. This shows that cutting setup delays can matter as much as speeding up the task itself, while shared warm capacity avoids keeping whole GPUs idle.