Kaggle Notebook concurrent sessions and shared weekly GPU quota

Question

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?

Tasadduq Burney · Answer

Direct answerMultiple Kaggle Notebooks running GPU or TPU sessions under one account are expected to draw from a single account-level weekly quota for that accelerator type. Concurrent sessions do not create separate quota pools, so two GPU notebooks running at the same time consume from the same remaining hours. Kaggle publishes no latency SLA, concurrency throttle threshold, or guaranteed start time, so execution latency under concurrent sessions cannot be confirmed as a platform-enforced concurrency limit. Container cold starts, image size, dataset loading, and shared host contention can slow a single session too.Likely explanation versus unconfirmed behaviorThe likely explanation for faster quota depletion with concurrent notebooks is wall-clock consumption: two sessions running simultaneously use twice the quota per hour of real time, even if the per-hour rate per session is unchanged. Whether latency appears only under concurrent requests is unconfirmed; Kaggle does not document concurrency-specific throttling, and the weekly quota may not be the only limit on concurrent execution.Verification steps for this caseOpen Kaggle account usage or quota settings and record remaining GPU hours for your accelerator type.Run one GPU notebook with internet disabled, using fixed dataset, code, and GPU type; record start-to-first-output time and quota consumed after a fixed interval.Start two identical GPU notebooks at the same time under the same conditions and compare quota drain and latency with the single-session run.Note any quota-exhaustion or resource-unavailable errors and the weekly reset schedule shown in account settings.CPU notebooks are not governed by the GPU quota.Assumptions and uncertaintyExact weekly hours, reset day and time, concurrent-session caps, and paid-tier quotas are version-sensitive and not verified for the current platform. Treat the steps above as a controlled check, not a guaranteed platform contract.Missing diagnostic detailAre you seeing quota-exhaustion or resource-unavailable errors when starting the second GPU notebook, or only slower execution? That determines whether the issue is quota depletion or unrelated latency.

Kaggle Notebook concurrent sessions and shared weekly GPU quota

Goal

Constraints and uncertainty

1 answer

Direct answer

Likely explanation versus unconfirmed behavior

Verification steps for this case

Assumptions and uncertainty

Missing diagnostic detail

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