Jungle Grid

This is exactly the workflow Jungle Grid is built for: Build the app wherever you want. Send the AI workload to Jungle Grid. We handle the GPU execution layer.

For LoRA Pilot sessions, the goal is simple: Launch from Jungle Grid Open the training dashboard Upload data Train Download the LoRA output No raw provider URLs. No messy GPU setup.

Agents should not only suggest commands. They should be able to execute real workloads. That’s why Jungle Grid includes an MCP execution layer for submitting, monitoring, and retrieving logs from AI jobs. junglegrid.dev

Jungle Grid is building the execution layer for AI workloads. Submit inference, training, batch, or creator workloads by intent. We handle placement, routing, logs, artifacts, retries, and runtime orchestration. junglegrid.dev

Submit the workload. Let Jungle Grid handle execution. 🌿⚡ AI inference, training, fine-tuning, and batch jobs without managing GPU providers or infra. Join the community: discord.gg/kpJqxXFFCs

Inference, training, batch jobs, agent workloads. One layer to submit them. One system to route and execute them.

No pod configs. No cluster babysitting. No GPU drama. Submit the workload. Move on.

Stop picking GPUs manually. Jungle Grid handles placement across available GPU capacity.

Hope y'all claiming your free jobs?

10 free inference jobs are now available on Jungle Grid. We just crossed 150 users, so we’re giving every user 10 free inference jobs to run AI workloads without picking GPUs or managing infra. Sign up to claim yours 👇 junglegrid.dev

We just launched Jungle Grid on Orynth 🌴 Built it because running AI workloads shouldn’t require picking GPUs, regions, or providers. You just describe the workload → it runs. Still early would love feedback from builders: orynth.dev/projects/jungl… CA: S2jbenDyZAkAQKeWRSaSf3i83txR4z3m3S9akVmyory

100+ workloads run. real jobs. real routing. real execution. appreciate everyone who’s been using Jungle Grid and sharing feedback we’re just getting started.

30+ failed GPU placements. same job. same request. Jungle Grid kept going until it found capacity and ran it. this is the layer most people are missing

“could not find any pods with required specifications” we hit this 30+ times on one job. normally: job fails with Jungle Grid: → retry → fallback → reroute → execute successfully infra shouldn’t break just because GPUs are busy

we tried to run a simple AI job today → 30+ placement attempts → T4, 3090, 4090 → no capacity across all Jungle Grid kept retrying in the background rerouted → found capacity → executed this is what “stop picking GPUs” actually means

@foundrceo junglegrid.jaguarbuilds.dev

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@foxtomb232 junglegrid.jaguarbuilds.dev

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