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What is AI Infrastructure?
Running AI models — especially large language models — requires specialized compute resources. Whether you're fine-tuning, deploying agents, or running inference, the right infrastructure makes or breaks your performance. AI Central breaks down the best platforms to get started fast.
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Top Cloud GPU Platforms
Provider
RunPod“Spin up GPU-powered containers for Stable Diffusion, LLaMA, and more. Great pricing for solo devs and research.”
Google Cloud“Access scalable GPU instances and integrate with Vertex AI. Ideal for enterprise pipelines.”
AWS EC2 / SageMaker“Robust for scaling models into production. Broad GPU support from T4 to H100.”
Paperspace“Simple UX for model hosting and training, now owned by DigitalOcean.”
Lambda Labs“Raw power for serious fine-tunes. Access A100s and RTX 6000s directly.”
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Nvidia Hardware
The GPUs powering modern AI aren’t one-size-fits-all. Here’s how today’s top chips stack up:
A100 – Cloud-scale model training
H100 – Transformer-tuned, the gold standard for new LLMs
RTX 4090 – Incredible consumer-level inference power
L4 / T4 – Good balance for smaller models & APIs
Whether you’re training or deploying, choosing the right chip matters.
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Best & Used By Pros:
Google's GCE, Amazon's AWS, Lamba Labs and RunPods
AI Infrastructure is the Key
Platform: Best For: Notable Feature:
RunPod Solo Dev Inference Quick-start templates
Google Cloud Scale & Integrations Vertex AI tools
Lambda Labs Raw Power Direct access to A100s
Want to run AetherPro:
Here's what you need-
AetherPro supports local, edge, and cloud-based agent deployment. Recommended minimum spec:
16GB RAM
1x T4 or RTX 3060
Fast SSD
Python 3.10+, Docker optional
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