> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mka1.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> <Badge color="blue" shape="pill">Early access</Badge>

Run training jobs and model servers on GPU capacity through MKA1 Compute.


Compute runs containers on the hardware available to your deployment. Use a **job** for work that finishes, such as training, and a **service** for a persistent process, such as a model server.

Compute schedules and observes the workload. Your container supplies the training or serving software.

## Choose a workflow

| Goal                                              | Guide                                                          |
| ------------------------------------------------- | -------------------------------------------------------------- |
| Store weights and training data                   | [Repositories](/docs/repositories)                             |
| Generate training data                            | [Datasets](/docs/datasets)                                     |
| Run a finite training workload                    | [Run a fine-tune job](/docs/compute-fine-tune-job)             |
| Train against a reward function                   | [Reinforcement learning](/docs/compute-reinforcement-learning) |
| Keep a model endpoint running                     | [Deploy a model server](/docs/compute-deployment)              |
| Inspect capacity, workloads, secrets, and volumes | [Manage resources](/docs/compute-resources)                    |

## How a workload runs

1. Select hardware from your deployment’s catalog and request a quote.
2. Supply a container image, command, and configuration.
3. Put credentials in Compute secrets and reference them from the workload.
4. Create a job or service and inspect its status, logs, and events.
5. Publish outputs to a repository or supported persistent storage.
6. Terminate persistent services when you finish using them.

A quote reports price and availability; it does not reserve hardware. Provider capabilities determine which storage and scaling options are available. Your organization must have Compute access enabled.

## Keep artifacts separate from the runtime

The container’s ephemeral disk is not an artifact repository. Training workloads should publish checkpoints and final outputs explicitly. [Repositories](/docs/repositories) gives jobs and model servers a shared identifier for those artifacts.

## Follow the feature status

Individual guides identify early-access or upcoming functionality. Those labels describe the specific workflow; they do not imply that every provider supports every option.