Kaggle Notebook concurrent sessions and shared weekly GPU quota
0 reputation · 08 Jul 2024, 14:45 UTC
Goal
Determine whether multiple Kaggle Notebooks running at the same time on one account draw from a single shared weekly GPU/TPU compute quota, and whether execution latency appears only under concurrent requests.
Constraints and uncertainty
Kaggle Notebooks run in isolated containers with CPU, GPU, or TPU resources assigned per session. Public documentation describes a weekly GPU and TPU quota that applies across all notebooks owned by one account, but it does not publish a latency SLA, concurrency throttling thresholds, or exact quota hours. Those values are version-sensitive and unverified for the current platform. Internet access is disabled by default per notebook and can independently affect external fetch latency.
Because concurrent slowdown is not a documented Kaggle feature or bug, the following behavior remains unresolved:
- Does starting two GPU notebooks simultaneously consume quota hours from the same weekly pool?
- Does notebook start or execution latency increase under concurrent sessions compared with a single session?
- Is the per-account weekly quota the only documented limit governing concurrent notebook execution?
