Computegpu

io.github.jysweePublished by GitHub account jysweev1.2.1Updated Oct 28, 2026

Rent cloud GPUs from your agent: live catalogue, deploy, stop, destroy. Per-second billing.

The publisher's own server: Vips

VerifiedStreamable HTTPWeb executableCloud & InfrastructureAI & ML

Overview

AI-generated overview

Lets an assistant rent cloud GPUs: browse the live catalogue, deploy, stop, resume and destroy instances, with per-second billing.

What you can do
  • Explain ComputeGpu and how to activate the server in the current session
  • Create an account from the human's email and return an API key that unlocks the remaining tools
  • Once activated, the catalogue, deploy, stop, resume, destroy, status and billing tools become available
When to use it
When you want an assistant to spin up a cloud GPU for model inference or Docker/SSH work without leaving the chat, and to pause or tear it down afterwards.
Requirements
Remote endpoint or local npm package run with npx (Node.js 18+). A ComputeGpu account and API key, passed as COMPUTEGPU_API_KEY or an Authorization bearer header; without a key the session starts in onboarding mode. The owner must verify a card ($0, no charge) before the first deploy.
Before you install
Deploying GPUs spends real money on per-second billing, and top-ups have a $1 minimum; spot instances can be preempted with a 5-minute warning. The API key is a secret, is shown only once at signup, and is stored in a config file on disk. Signup needs the human's email, and destroy permanently removes an instance.

Installation

In SourceWeft

  1. Open Computegpu in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.github.jyswee%2Fcomputegpu/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "mcpServers": {
    "computegpu": {
      "type": "http",
      "url": "https://mcp.computegpu.com/mcp"
    }
  }
}

README

computegpu

Rent cloud GPUs from your terminal. Deploy in 30 seconds.

bash
npm i -g computegpucomputegpu signup [email protected] my-agentcomputegpu deploy --gpu "RTX 4090"

Install

bash
npm i -g computegpu

Requires Node.js 18+.

Quick Start

bash
# Create accountcomputegpu signup [email protected] my-project
# Browse GPUscomputegpu types
# Deploycomputegpu deploy --gpu "RTX 4090" --models llama3.3
# Check statuscomputegpu podscomputegpu status POD_ID
# Stopcomputegpu stop POD_ID

Commands

CommandDescription
signup <email> <tenantId>Create account + get API key
login --key <KEY>Save existing API key
logoutClear saved key
meShow account info
typesList 47 GPU models with pricing
cheapest --vram <GB>Find cheapest GPU for VRAM
deploy --gpu "RTX 4090"Deploy on-demand instance
podsList active instances
status <podId>Instance status + billing
stop <podId>Pause (billing stops)
resume <podId>Resume paused instance
destroy <podId>Permanent destroy
balanceWallet balance
topup <amount>Request top-up ($1 minimum)

Deploy Options

bash
computegpu deploy --gpu "RTX 4090"           # on-demandcomputegpu deploy --gpu "RTX 4090" --spot    # 50% off (preemptible)computegpu deploy --gpu "H100" --count 4     # multi-GPUcomputegpu deploy --gpu "RTX 4090" --models llama3.3,gemma4  # pre-load modelscomputegpu deploy --gpu "RTX 4090" --type docker  # Docker + SSHcomputegpu deploy --gpu "RTX 4090" --volume 100   # persistent storage

Config

API key is saved to ~/.computegpu/config.json (global) or ./.computegpu/config.json (local).

Priority: --key flag > COMPUTEGPU_API_KEY env > local config > global config.

bash
computegpu login --key gpu_your_key          # save globallycomputegpu login --key gpu_your_key --local  # save per-projectcomputegpu config                             # show active config

Pricing

47 GPU models. Per-second billing. $1 minimum top-up.

  • Consumer: RTX 3060 ($0.22/hr) to RTX 5090 ($1.24/hr)
  • Datacenter: A40 ($0.64/hr) to H200 ($10.76/hr)
  • Spot: 50% off (preemptible with 5-min warning)

Links

License

Proprietary. Copyright (c) 2026 Tyga.Cloud Ltd. See LICENSE.

Source: README.md at commit 26e7efb

Tools

2

computegpu_get_started

Explains what ComputeGpu is and how to activate this MCP server (no API key on this session yet).

Takes no parameters.

computegpu_signup

Create a ComputeGpu account. Requires the human's email. Returns an API key (shown once) and unlocks all ComputeGpu tools in this session. Before the first deploy the owner verifies a card ($0, no charge). Ask the human for their email before calling.

Parametersemailrequiredtenantrequired

Version history

1
  1. v1.2.1LatestOct 16, 2026

Score

90% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 daysRequired25 pts
  • Auth declaredRequired10 pts
  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

    Declare a license in the repository or the registry entry.

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