# Amazon — Lend and borrow resources across teams with AWS Batch’s quota management for SageMaker Training jobs

- Company: Amazon (amazon.com)
- Announced: 2026-10-29T16:58:57+00:00
- Category: capability-change
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://aws.amazon.com/blogs/machine-learning/lend-and-borrow-resources-across-teams-with-aws-batchs-quota-management-for-sagemaker-training-jobs/
- Record: https://forck.live/items/29435-lend-and-borrow-resources-across-teams-with-aws-batch-s-quota-management-for
- Subject: Bedrock / Nova

AWS added quota management to AWS Batch, letting administrators divide shared GPU capacity into quota shares for teams, projects or workloads. The company says customers managing multi-team training environments asked for a way to define per-team allocations, share idle capacity automatically and run priority workloads first. Each quota share carries capacity limits and a resource-sharing strategy: RESERVE keeps idle compute for the share, LEND lends it to others, and LEND_AND_BORROW both lends idle capacity and borrows when the share has work. A quota share acts as a virtual queue nested inside a job queue, and AWS Batch prioritizes shares that are under their limits as it iterates through them. When jobs arrive in a share that lent out capacity, cross-share preemption reclaims the resources, so administrators do not have to find and terminate borrowing jobs themselves. Priority-based in-share preemption can also be turned on to preempt lower-priority jobs in the SCHEDULED, STARTING or RUNNING state. Setting it up requires a Scheduling Policy with a quotaSharePolicy, a Service Environment specifying instance capacity, and a SageMaker Training job queue connected to both; the example uses the SageMaker Python SDK v3 and three quota shares each capped at one ml.g5.xlarge instance.

## Evidence

Verbatim from https://aws.amazon.com/blogs/machine-learning/lend-and-borrow-resources-across-teams-with-aws-batchs-quota-management-for-sagemaker-training-jobs/:

> With the launch of AWS Batch support for Amazon SageMaker Training jobs, customers gained a new fully managed way to queue, schedule, and retry machine learning (ML) training workloads without building their own infrastructure. Instead, they use the queuing and scheduling features of AWS Batch. As organizations scale their artificial intelligence and machine learning (AI/ML) operations and onboard more teams, coordinating access to shared graphics processing unit (GPU) capacity can introduce additional communication, coordination, and configuration overhead for administrators. This overhead includes setting up separate queues, adjusting capacity limits, and carefully orchestrating the right workloads to get the right resources at the right time. A core value proposition of AWS Batch is helping customers increase utilization of their compute, and as training environments grow, so does the opportunity to do more with existing capacity.
> 
> Working backward from customers managing multi-team training environments, we heard a consistent request: give us a way to define allocations per team or project, share idle capacity automatically, and run our highest-priority workloads first.

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