I will rent you cloud GPU servers for AI — H100 / A100, hourly, ready fast

Rent a ready-to-go cloud GPU server for AI work — H100 or A100, billed by the hour, provisioned fast and handed to you with SSH so you can start training or running inference today.

What you get

A dedicated cloud GPU instance configured for real AI workloads, not a barebones box you have to fight with for an afternoon. Every order includes:

• Your choice of GPU: NVIDIA H100 (80GB) for the heaviest training and large-model inference, or A100 (40GB or 80GB) for cost-aware training, fine-tuning, and batch jobs. Single-GPU or multi-GPU nodes available.
• Hourly rental — spin up for an experiment, keep it for a sprint, or scale a run; you tell me the window and I provision for exactly that.
• Pre-installed AI stack: recent NVIDIA drivers, CUDA, cuDNN, and your choice of PyTorch or TensorFlow, plus nvidia-smi verified working before handoff.
• Root SSH access and an optional Jupyter / VS Code Server endpoint so you can connect however you prefer.
• Fast NVMe storage for datasets and checkpoints, sized to your tier, with guidance on mounting external buckets (S3, GCS, R2) for larger corpora.
• Sane networking: a public IP, open ports you request, and high-bandwidth egress so pulling models from Hugging Face or pushing checkpoints doesn't crawl.
• Environment file documenting exactly what's installed, the GPU/driver/CUDA versions, and how to reconnect — no guessing.

Plans

Tier	GPU & node	Best for	Included
Starter	Single A100	Fine-tuning, inference, prototyping	Pre-installed PyTorch/CUDA stack, SSH, NVMe scratch, setup verification
Growth	Single H100 or dual A100	Serious training runs, larger models, longer windows	Everything in Starter + Jupyter/VS Code endpoint, larger NVMe, bucket-mount help, checkpoint guidance
Scale	Multi-GPU H100 node	Distributed training, big-model workloads, team runs	Everything in Growth + multi-GPU NCCL config, priority provisioning, persistent storage option, hands-on launch support

Need a specific GPU count, region, or longer reservation? Message me and I'll tailor the node before you order.

How it works

1. Tell me your job — GPU type (H100 or A100), how many, framework, and roughly how long you need it.
2. I confirm availability and the exact spec, then provision the instance.
3. I install and verify the stack — drivers, CUDA, your framework — and run nvidia-smi plus a quick GPU sanity check.
4. You get credentials: SSH details, the optional notebook URL, and the environment file describing everything.
5. You run your workload — I'm on hand for connection issues, mounting data, or multi-GPU launch questions throughout your window.
6. You wrap up; I help you pull checkpoints and tear the box down cleanly so nothing lingers.

Why choose this

• Ready fast — the title isn't a slogan. You get a working, GPU-verified box, not a raw VM and a wiki link.
• Real H100 / A100 silicon, not a shared slice or a mystery accelerator. You know exactly what you're running on.
• Hourly, no lock-in — rent for one experiment or a long run; you're not buying a month to test an idea for a day.
• Configured for AI, by someone who runs these workloads — CUDA/driver/framework mismatches are handled before you ever log in.
• Direct support during your rental, so a stalled pip install or an NCCL error doesn't eat your afternoon.

Who it's for / use cases

• ML engineers fine-tuning LLMs who need an H100 for a weekend without a cloud-console rabbit hole.
• Founders shipping an AI feature who want inference capacity live today, not after a quota-increase ticket.
• Researchers running training sweeps who need A100s by the hour and predictable, documented environments.
• Indie devs and small teams training diffusion or vision models who want NVMe-backed boxes without managing infrastructure.
• Anyone hitting a wall on a laptop or a single consumer GPU and needing serious VRAM right now.

FAQ

Q: Can I choose between H100 and A100? Yes — tell me which fits your workload and budget, and I'll provision that exact GPU; I'm happy to advise if you're unsure.
Q: How fast can I get access? Most single-GPU boxes are provisioned and handed over quickly after we confirm spec and availability; multi-GPU nodes may take a little longer.
Q: Is billing really hourly? Yes — you specify your window and I provision for it; rent for an experiment or a longer sprint, no month-long commitment required.
Q: What's pre-installed? NVIDIA drivers, CUDA, cuDNN, and PyTorch or TensorFlow (your pick), all verified with nvidia-smi before you connect.
Q: How do I connect? Root SSH by default, plus an optional Jupyter or VS Code Server endpoint if you'd rather work in a browser.
Q: Can I run multi-GPU distributed training? Yes — the Scale tier ships multi-GPU H100 nodes with NCCL configured, and I help you launch your distributed run.
Q: What about my datasets and checkpoints? You get fast NVMe scratch storage, and I'll help you mount S3, GCS, or R2 buckets and pull checkpoints out before teardown.
Q: Do you offer persistent storage between sessions? On the Scale tier I can attach a persistent volume so your environment and data survive across rentals — just ask.