I was leading a project running a bunch of AI jobs.
The models weren't huge, but our compute bill kept growing.
Turns out the problem wasn't the models — it was how we were running them.
The real issue
Every job came with decisions like:
- A100 or 4090?
- Will this fit in VRAM?
- Which provider is available right now?
And every wrong decision had consequences:
- overpaying for hardware
- OOM crashes
- retrying jobs across providers
- time wasted debugging infra
We weren't building AI.
We were managing GPUs.
The shift
At some point I stopped trying to optimize setups and asked:
Why are we choosing GPUs at all?
Why does every dev need to think about hardware, providers, capacity, and pricing just to run a job?
What I built instead
I built Jungle Grid — a simple way to run AI workloads without dealing with GPUs.
Instead of picking hardware, you just describe the workload.
Inference example:
jungle submit --workload inference --model-size 7
Batch example:
jungle submit --workload batch --image python:3.11 --command python script.py
That's it.
- No GPU selection
- No provider guessing
- No infra setup
What happens under the hood
- Workload classification
- GPU selection across providers
- Routing based on cost / latency / reliability
- Automatic retries + failover
- Lifecycle tracking
There's also an API if you want to integrate it into your own services.
What changed
- Most inference jobs now cost ~$0.01–$0.05
- No more failed runs due to wrong hardware
- No more time wasted debugging infra
But the biggest win is focus.
We went from:
"Will this run?"
to:
"What should we build next?"
Takeaway
The hard part isn't running AI.
It's all the decisions before execution.
Remove those — and everything gets simpler.
If you're running AI workloads, how are you handling GPUs today?
Top comments (0)