> ## Documentation Index
> Fetch the complete documentation index at: https://docs.onebucket.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Cache next to compute

> GPUs in one cloud, data in another: present the data local to the job

## The situation

Training or inference runs in a GPU cloud, while the data sits in AWS, in another cloud and on-premises. Jobs wait on remote reads, GPUs sit idle, and every epoch pays egress again. Moving the data wholesale is not an option.

## What OneBucket changes

A cache-mode peer bucket sits in the GPU cloud, under the same namespace. Prefetch warms it before a job, by policy or on request. Reads are served locally and stay in sync with the source. The source remains the system of record and nothing is rearchitected.

![Training jobs in a GPU cloud read through OneBucket from a local cache-mode peer, with AWS S3 as the source of record and on-prem archive read on demand](https://mintcdn.com/onebucket/IRmZGtHTOOl3yo0n/images/scenarios/cache-near-compute.png?fit=max\&auto=format\&n=IRmZGtHTOOl3yo0n\&q=85\&s=a032ae817783deac973e14a002cd5a78)

## How it works

* **Cache-mode peer.** Add a peer bucket in `cache` mode located at the GPU provider. It holds a copy of what has been read or prefetched; the source stays authoritative.
* **Prefetch.** Warm the cache ahead of a run with the `prefetch` tool over MCP, or by policy. Warming is asynchronous: a destination may not be readable the instant prefetch is queued.
* **Local reads.** Jobs read the same path they always did; the read is served from the cache. Data crosses the cloud boundary once rather than once per job.

## Related

* [Set up your OneBucket](https://docs.onebucket.io/setup.md): cache mode
* [Claude connector](https://docs.onebucket.io/connectors/claude.md): `prefetch`, `locate`
* [Placement by policy](https://docs.onebucket.io/scenarios/placement.md)


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