Jungle Grid

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Inspiration

Running AI workloads today is unreliable. Jobs fail, GPUs are unavailable, queues stall, and developers waste time debugging infrastructure instead of building.

We kept seeing the same pattern:

• A run fails → retry → suddenly works
  
• No config change → just capacity showing up
  

The problem isn’t access to GPUs. It’s fragmented, unreliable execution.

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What it does

Jungle Grid is an intent-based execution layer for AI workloads and agents.

You don’t pick GPUs.
You describe the workload.

The system:

• Classifies the workload (inference, training, batch)
  
• Selects compatible GPU options
  
• Routes across multiple providers
  
• Retries automatically until it finds a viable run
  

We also introduced an agentic layer (MCP):

• Agents can submit workloads directly
  
• Execution becomes part of autonomous workflows
  
• No human-in-the-loop infrastructure decisions
  

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How we built it

• Go backend orchestrator for scheduling, routing, and failover
  
• Redis for job queue and real-time state
  
• PostgreSQL for persistence (jobs, nodes, users)
  
• Scoring engine using price, latency, reliability, and availability
  
• Node agent (distributed compute layer) for external GPU providers
  
• CLI + API for submission and integration
  
• Integrated with managed GPU providers for real execution
  

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Challenges we ran into

• Capacity fragmentation: GPUs exist, but not where/when you need them
  
• Provider inconsistencies: different failure modes, APIs, and behaviors
  
• Cold starts & queue delays: unpredictable execution timing
  
• Image/runtime mismatches: jobs failing due to environment issues
  
• Designing a system that keeps trying without failing prematurely
  

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What we learned

• Reliability matters more than raw compute
  
• Developers don’t want GPUs—they want completed workloads
  
• The future isn’t GPU selection—it’s intent-based execution
  
• Agent-driven systems need infrastructure abstraction, not exposure
  

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What’s next

• Expand provider coverage
  
• Improve scheduling with real-time latency + region awareness
  
• Deepen agent (MCP) integration for autonomous execution
  
• Build a global distributed supply layer via node agents
  

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Try It Out

👉 https://junglegrid.jaguarbuilds.dev/

Built With

• cli-tooling
• distributed-systems
• docker
• github-actions
• go
• gpu-compute-(cuda)
• multi-provider
• node.js
• postgresql
• redis
• rest-apis
• typescript