This is week 24 of my “I Used It for a Week” series. Last week I reviewed Langfuse, the LLM observability platform. This week: the GPU cloud that makes local AI practical.
RunPod is a GPU cloud designed for AI workloads. If you want to run large models but don’t have the hardware, RunPod rents you GPUs by the hour.
After a week of running models on RunPod, I think it’s the best option for developers who need GPU power without buying hardware.
How It Works
- Sign up at runpod.io
- Choose a GPU (A100, H100, etc.)
- Deploy a pod (pre-configured with CUDA, PyTorch, etc.)
- SSH in and run your models
- Stop the pod when done (pay only for hours used)
The pricing is straightforward: $0.40-2.50/hour depending on the GPU. No minimum commitment, no monthly fees.
Day 1: First Impressions
I deployed an A100 80GB pod with Ubuntu, CUDA 12, and PyTorch pre-installed. The pod was ready in about 3 minutes.
I installed Ollama and pulled Llama 4 Maverick. The download took about 5 minutes. Inference was fast: 40-50 tok/s.
The experience was similar to running locally, but with more power. An A100 80GB is more VRAM than most consumer GPUs.
Day 2-3: Model Hosting
RunPod’s killer use case: hosting models for API access. I deployed a vLLM server with Qwen 3.6 27B and exposed it via a public endpoint.
Now I have a private API endpoint that I can use with any tool. Aider, MiMo Code, Continue.dev — they all connect to it like any OpenAI-compatible API.
The cost: about $0.50/hour for the A100. For 8 hours of coding per day, that’s $4/day. Compare to $50-100/month for cloud APIs.
Day 4-5: Storage and Snapshots
RunPod offers persistent storage and pod snapshots. You can save your model downloads and environment setup, then restore them when you need a new pod.
This matters because downloading large models takes time. With snapshots, you download once and reuse.
What Blew Me Away
Price-performance
RunPod’s pricing is competitive. A100 80GB at $0.40/hour is cheaper than most cloud providers for equivalent GPU access.
Ease of use
Pre-configured pods mean you don’t spend hours setting up CUDA and drivers. SSH in and start working.
Persistent storage
Snapshots and persistent volumes save time on model downloads and environment setup.
Community
RunPod has a growing community of AI developers. Their Discord is helpful for troubleshooting.
What Frustrated Me
Cold starts
Starting a pod takes 2-3 minutes. If you need instant access, this is annoying. Compare to LM Studio which is always ready.
Internet dependency
Unlike local models, RunPod requires internet. If your connection is slow or unreliable, the experience suffers.
Data privacy
Your code and models are on RunPod’s servers. For proprietary code, this is a security consideration. Compare to local deployment where everything stays on your machine.
No GPU selection granularity
You choose GPU type but not specific GPU models. An A100 80GB is an A100 80GB, but performance varies between data centers.
Networking costs
Data transfer out is free within the same region, but cross-region transfer costs extra. If you’re accessing your pod from far away, latency adds up.
Use Cases
Running large models locally. If you want to run Llama 4 Maverick or DeepSeek V4 but don’t have 64GB+ RAM, RunPod gives you an A100 80GB for $0.40/hour.
Model hosting. Deploy a vLLM server with your favorite model and expose it as an API. Use it with Aider, MiMo Code, or any OpenAI-compatible tool.
Training and fine-tuning. RunPod’s persistent pods work well for training runs that take hours or days. Save checkpoints to persistent storage.
Experimentation. Try different models and configurations without committing to hardware purchases.
RunPod vs Lambda vs AWS
- RunPod: Best price-performance, easiest setup, AI-focused. But requires internet.
- Lambda: Similar pricing, more enterprise features. But less community.
- AWS: Most features, most expensive, most complex. Overkill for most AI workloads.
RunPod is the best option for individual developers and small teams.
Cost Comparison
I tracked my RunPod costs over the week:
| Task | Hours | GPU | Cost |
|---|---|---|---|
| Running Llama 4 Maverick | 10 | A100 80GB | $4.00 |
| Model hosting (API) | 40 | A100 80GB | $16.00 |
| Experiments | 5 | A100 80GB | $2.00 |
| Total | 55 | $22.00 |
Compare to:
- LM Studio: $0 (but limited by local hardware)
- OpenAI API: $50-100/month for similar usage
- Anthropic API: $50-100/month for similar usage
RunPod fills the gap between local and cloud. More power than your laptop, cheaper than API costs.
Would I Keep Using?
Yes. RunPod is now my go-to for running large models that don’t fit on my local hardware. The pricing is right and the setup is easy.
Rating: 8.5/10 — Best GPU cloud for individual developers. Cold starts and internet dependency are minor issues.
FAQ
What is RunPod?
RunPod is a GPU cloud designed for AI workloads. It offers on-demand GPU access (A100, H100, etc.) with pre-configured environments for running AI models.
How much does RunPod cost?
$0.40-2.50/hour depending on the GPU. An A100 80GB costs about $0.40/hour. You only pay for hours used.
Can I run large models on RunPod?
Yes. A100 80GB can run models up to about 70B parameters with quantization. H100 pods can run larger models.
Is RunPod better than running locally?
RunPod is better when you need more VRAM than your local hardware provides. Local is better for privacy, latency, and zero ongoing costs.
Related: RunPod Complete Guide · How Much VRAM for AI · Best Local AI Models 2026
Next week: I Used Open WebUI for a Week — the self-hosted ChatGPT alternative.