Up to $20Free credits →For AI developers, ML engineers, and agent builders

Run Llama, Stable Diffusion, and AI agents no GPU setup, no cloud accounts.

One command to submit any AI job. We pick the GPU, spin it up, stream logs, and auto-recover on failures.

Sign up to claim up to $20Use via API / Agents

If you've ever used Google Colab, Jungle Grid is like that, but with one simple command instead of a notebook, and we handle all the GPU details.

$npx @jungle-grid/cli@latest login
For AI agentsnpx @jungle-grid/mcp
No GPU knowledge requiredUp to $20 prize draw, no card needed for signup

Execution overview

Workload routinghealth-aware placement
Failure handlingauto requeue active
Operator visibilitylive logs + status events
Monitoring24/7
Retry policy3X
112msDispatch

Try Jungle Grid

Try Jungle Grid without paying

Inference only

Job setup

01Describe02Review03Preview

Run status

Shape the job before you commit

Workloadinference
Registry hostdocker.io
Access modepublic image
Precisionfp16
Model size18 GB

Fill in the job details, then continue to launch it after signup.

One surface in, healthy GPU out

Agents and developers submit jobs. Jungle Grid routes them to healthy GPUs.

The request stays simple on the left. The routing work happens in the middle. Capacity, health, and execution state stay visible before the workload lands on a machine.

Intent-awareHealth-awareLoad balanced
Agents and developers submit jobs into Jungle Grid, which routes requests to healthy GPU targets.
Submitdevelopers, agents, apps
Routefit + health + cost
Runhealthy capacity only

Who uses Jungle Grid

ToolHow you use itWhat you manage yourself
Google ColabBrowser notebook, click "Run"Code, environment, session timing
AWS / RunPodRent raw GPU machines, manage everythingMachines, GPUs, regions, scaling
Jungle GridRun AI jobs with 1 command or simple APIOnly your prompts / tasks. We choose GPUs and providers.

You don't need any separate cloud or GPU accounts. You only sign up for Jungle Grid.

AI engineers

Run AI jobs — text, image, audio, or training

  • CLI / SDK
  • Submit jobs, get estimates, logs + results
  • No GPU or provider accounts to manage
Get the CLI →
Apps and agents

Add AI jobs to your app or automation

  • REST API or AI agent hook
  • Trigger from your app, workflow, or AI agent
  • No GPU or provider code in your app
Explore the API →
New to AI compute?

Start without knowing anything about GPUs

  • Up to $20 prize draw - no card required for signup
  • Copy-paste your first job in 3 minutes
  • Plain-English beginner guide on /learn
Beginner path →Have your own GPUs? Connect them and earn → (advanced)

What Jungle Grid actually does

Reliable execution across fragmented GPU capacity

Routing story4 checks

Intent

Describe the workload, not the hardware

Tell Jungle Grid what you want to run and how big the model is. The platform figures out which GPU fits, without you picking a provider, region, or hardware type.

VRAM fit

Confirms your job will fit before it starts

Before your job runs, Jungle Grid checks that the machine has enough memory to handle it. If it doesn't fit, you get a clear message — not a job that hangs silently forever.

Signal scoring

Picks the best machine, not just a random one

Routing looks at price, reliability, how busy the machine is, its response speed, and whether it's overheating — all before placing your job. You never land on an obviously bad machine.

Recovery

If a machine fails, your job keeps running

When a machine goes offline, overheats, or becomes unreachable mid-run, your job is automatically moved to a healthy machine. You don’t need a manual backup plan.

01

Intent

Describe the workload, not the hardware

  • Say "7B inference job" instead of naming a specific GPU model.
  • Pick cheapest, fastest, or balanced — only when you want to steer placement.
  • Check job status from the CLI or portal instead of logging into provider dashboards.
02

VRAM fit

Confirms your job will fit before it starts

  • Checks available memory on the machine before selecting it.
  • Shows a clear rejection instead of a job stuck in the queue with no explanation.
  • Saves you from burning time waiting on a run that was never going to work.
03

Signal scoring

Picks the best machine, not just a random one

  • Balances cost, machine health, queue load, and speed in one decision.
  • Works across a mix of different GPU types without you tuning each provider.
  • Skips degraded machines before they silently ruin a run.
04

Recovery

If a machine fails, your job keeps running

  • Detects when a machine goes down without you checking provider consoles.
  • Moves affected jobs back into the queue with their status clearly shown.
  • Long-running jobs don’t depend on a single machine staying healthy.

For AI agents & agent frameworks

MCP (Model Context Protocol) lets AI coding tools like Claude Desktop and Cursor run Jungle Grid jobs directly. If you’re just submitting jobs yourself, you don’t need this — use the CLI or portal instead.

Add Jungle Grid to your MCP host in one block.

Paste this under your host's mcpServers section to expose Jungle Grid inside agent workflows.

Claude DesktopCursorWindsurfAny MCP-aware host
See the full MCP setup guide →
Paste under `mcpServers`Replace `jg_` with a real Jungle Grid API key.
"junglegrid": {
  "command": "npx",
  "args": [
    "-y",
    "@jungle-grid/mcp"
  ],
  "env": {
    "JUNGLE_GRID_API_KEY": "jg_"
  }
}

Routing behavior

How Jungle Grid avoids bad placements and stalled jobs

A GPU cluster map showing Jungle Grid selecting capacity across a pooled hardware layout.Capacity selection

Memory check

Confirms your job fits before it starts

Jobs that won't fit in the machine's memory are rejected immediately with a clear message, instead of sitting in the queue silently forever.

  • Memory is checked before the job is placed.
  • You see a clear message instead of a job stuck with no explanation.
  • No wasted time waiting on a run that was never going to work.
A health-aware routing diagram that sends workloads through healthy paths to available GPU clusters.Health-aware routing

Price + Latency

Placement signals

Scheduler scoring blends cost, queue depth, reliability, latency, and thermal state so you do not guess hardware manually or land on obviously degraded nodes.

  • Cost and speed are scored together.
  • Reliability and queue pressure stay in the decision loop.
  • Thermal and health signals protect active runs.
An operations console monitoring routing failures and recovery signals across a GPU network.Failure monitoring

Auto Requeue

Failure recovery

When a node drops or goes stale, affected jobs are requeued onto healthy capacity automatically instead of dying with the first bad placement.

  • Node failure does not end the workflow by default.
  • Affected jobs return to healthy routing paths.
  • Recovery stays visible from the same control plane.

Compute network

Absorb fragmented capacity, not just one cloud.

Jungle Grid dispatches across managed providers and independently operated nodes, absorbing fragmented capacity into one execution surface so failed provider paths do not turn into manual fallback work.

Control plane view

One operator surface watches routing, recovery, and expanding capacity.

Jungle Grid keeps provider sprawl and independent node growth under one operational surface. Healthy pools stay visible, noisy paths are isolated, and new GPU inventory folds into the same control plane.

Score live provider and node health before dispatch.
Watch recovery paths from the same operator console.
Add more fragmented supply without adding more dashboards.
An operator routing dashboard showing global routing paths, compute health, and control-plane monitoring.
A branded Jungle Grid GPU aisle inside a data center.

Managed providers

Consumer + data center

Largest GPU spot marketplace. Broad fallback capacity across regions and hardware classes.

Spot marketplace

Community-driven GPU rental. Useful spillover capacity when tighter clouds cannot place the workload.

ML-optimised cloud

Purpose-built ML cloud. A100 and H100 pools for heavier jobs that need predictable storage and networking.

Enterprise HPC

Kubernetes-native HPC cloud. Adds more controlled capacity when noisier pools are not a fit.

Sustainable compute

Low-carbon GPU cloud that broadens regional coverage and supply diversity.

+ Independent nodes
Decentralised · 247 nodes live

Independent nodes join the same execution pool.

Independent operators register nodes directly. The orchestrator validates hardware signals, measures latency, and folds those nodes into the same dispatch pool automatically. New capacity shows up without giving users another provider workflow to manage.

247Nodes online
18Countries
34GPU models
112msAvg dispatch
Register your node
jungle · node setup
$jungle node register --dispatch-url http://0.0.0.0:8090 --location eu-west
→ Measuring latency… 42ms
→ Validating GPU signals… ok
→ Payout account linked ok
✓ Node registered rtx-4090
$jungle node start --daemon
→ Installing node-agent… ok
✓ Daemon running pid 14822

Popular research paths

Start where high-intent GPU and model searches actually begin

The fastest route into Jungle Grid is usually not the homepage. It is a concrete question about model fit, inference cost, deployment workflow, beginner AI compute, or how Jungle Grid compares with a platform already on the shortlist.

Start hereLearn AI compute without starting from hardware hypeGive new developers a clear path through AI inference, beginner compute planning, and when routed execution starts to matter.
Model fitLLaMA 3.1 70B GPU requirementsRoute straight into one of the highest-intent model sizing queries instead of starting from a generic hub.
Model costCost to run Gemma 2 27BLand on a model-specific cost page when the real question is budget, not category theory.
Deployment guideBest way to deploy open-source LLMsMeet builders at the point where deployment friction becomes a product problem.
Architecture guideMulti-provider GPU orchestrationExplain why teams move routing logic above provider choice once workloads diversify.
ComparisonJungle Grid vs RunPodCatch shortlist traffic comparing a routing layer against direct provider capacity.
ComparisonJungle Grid vs Fireworks AIFrame Jungle Grid against adjacent inference platforms that AI builders already know.
Start with the beginner hubBrowse model pagesRead the guides

Changelog

Recent updates

View all changes →
May 8, 2026
Managed runtime diagnostics and startup probe failure tracing
Provisioning and managed-runtime flows now surface startup probe failures, callback diagnostics, and clearer runtime logging for broken launches.
May 6-7, 2026
Auth, device login, portal loading, and notification surfaces
The platform added device login, Google OAuth, password reset, redirect-aware auth flows, shared execution loaders, notifications, credentials pages, and stronger portal UX.
May 4-5, 2026
Provider controller, warm pools, and health-aware routing
The compute layer gained provider-controller orchestration, warm-pool provisioning, richer provider health snapshots, improved candidate resolution, and stronger RunPod / Vast.ai integration.
May 3-6, 2026
Billing, artifacts, API keys, and operator controls
Wallet and billing rails matured alongside artifact downloads, developer controls, API key management, control-plane badges, and admin/operator account tooling.

Demos

See the workload route before you read the rest.

CLI demoIntent-first terminal submit

Stop Choosing GPUs. Just Run the Workload (CLI Demo)

A straight operator flow in the terminal: describe the workload, submit by intent, and let Jungle Grid place the run on compatible GPU capacity.

Shows: Operator-first submission through the CLIWatch on YouTube
Claude demoNatural language to GPU execution

Describe a Workload in Claude → It Runs on GPUs

A Claude-driven workflow where a natural-language workload request becomes a real GPU-backed execution path through Jungle Grid.

Shows: Agent-led workload execution from ClaudeWatch on YouTube

FAQ

Frequently asked

Describe the workload. Let Jungle Grid route the execution.

New accounts can draw up to $20 in compute credits. Create an account, then deposit at least $10 to claim it.

Sign up to claim up to $20
No card required to start$5 default spending cap for new accountsWe never train on your dataBilled only through Jungle Grid - no provider feesCancel any job at any time
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