# Dstack Prototyping

Use with the dstack skill for model-serving work when the image, serving command...

- Repository: https://github.com/dstackai/dstack (2298 stars)
- Category: Media
- License: MPL-2.0
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add dstackai/dstack --skill dstack-prototyping -g -a claude-code -y`
Codex: `npx skills add dstackai/dstack --skill dstack-prototyping -g -a codex -y`
Cursor: `npx skills add dstackai/dstack --skill dstack-prototyping -g -a cursor -y`
Gemini CLI: `npx skills add dstackai/dstack --skill dstack-prototyping -g -a gemini-cli -y`

## Description

Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven. Guides task-first prototyping on real hardware, choosing fleets/backends that can reuse idle instances and caches, checking vLLM/SGLang sources, and verifying the final dstack service with a model request.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# dstack Prototyping

Use `/dstack` for CLI commands, YAML fields, apply/attach behavior, service URLs,
and other dstack syntax. This skill explains how to use dstack runs while the
model-serving configuration is still unknown.

## Goal

Find a working dstack service configuration for the requested model.

Before submitting a service, use a task on real hardware to test the serving
image, install/runtime assumptions, model download, cache path, command, port,
launch flags, resources, env vars, backend/fleet choice, and local model
request. Then submit the same configuration as a service and verify the model
through the dstack service URL.

## Choose Where To Run

Pick the offer whose hardware best fits the goal at hand. Only when several offers fit comparably, choose a VM-based backend, an SSH fleet, or a Kubernetes fleet: they support idle instances and/or instance volumes, so later runs reuse the provisioned/idle instance or instance volumes for caching model weights (and possibly other writes), while container-based backends start clean on every run.

Fetch `https://dstack.ai/docs/concepts/backends.md` and classify backends
from the fetched document, not from memory.

If the intention is to use PD disaggregation, the fleet must use `placement: cluster`. Since PD disaggregation implies running a router, unlike workers that must run on GPUs, the router normally should run on a CPU instance. Use `dstack fleet` to see existing fleets and `dstack fleet get <fleet name> --json` to inspect a specific fleet.

## Check Serving Sources

Check serving-framework sources early enough to choose the image, command,
launch flags, resources, cache paths, request format, and expected model
behavior.

For vLLM and SGLang, use these as credible sources:

The rest: https://github.com/dstackai/dstack/blob/HEAD/skills/dstack-prototyping/SKILL.md