GPU cloud for AI startups, with predictable monthly spend

A GPU cloud for AI startups has two jobs: give your engineers the GPUs they need this week, and give whoever owns the runway model a number that will not move. We rent dedicated GPU servers by the month, from one RTX 5090 at $449 to 8-GPU HGX B300 nodes, at a flat price billed in advance, with a one-month minimum and no long contract. Every GPU is a whole physical card assigned to your team only.

What to look for in a GPU cloud for AI startups

Industry billing models range from per-second on-demand and interruptible capacity to reserved terms and monthly dedicated servers. We offer only the last: you request a server, we confirm it in writing, you pay for the month and get root access over SSH. That gives up the flexibility of per-second billing in exchange for three things early teams tend to need:

• Capacity you keep. In 2026, GPU capacity is tight, many providers prefer one-year contracts and on-demand stock is often sold out. A monthly server stays yours from month to month for as long as you renew.
• A price that holds. FullGPU's October 2026 survey of public price lists found that, over the past year, the median on-demand price per GPU-hour rose by roughly 13% for the H100, 27% for the H200 and 20% for the B200. Our price is fixed for each paid month, and we give at least 30 days' notice before any change for future months.
• No surprise line items. No setup fee, and no egress fees within 20 TB a month on single-GPU plans or 50 TB on 8-GPU servers. Hyperscalers typically bill egress separately and require quota approvals for GPU instances.

Start with one GPU, scale to 8-GPU nodes

A quick sizing rule: model weights take about 2 GB per billion parameters in BF16 and 1 GB in FP8, and you should leave 10–30% headroom for activations and KV cache. When a model no longer fits, move up a card rather than fighting out-of-memory errors. Scaling up usually means adding or switching servers, so keep your environment in containers and your data portable from day one. When you run several servers, private networking between them is available on request. The workload pages go deeper: GPUs for LLM fine-tuning, GPU servers for LLM inference and GPU servers for LLM training.

Budget math for your runway

A team serving a 70B model on one H200 and experimenting on one H100 pays $3,090 + $2,190 = $5,280 a month, or $63,360 over twelve months, with no egress bill up to 20 TB per server per month. Unless you add or remove servers, the bill stays flat from month to month, which keeps the runway model simple.

The catch is utilization. An H100 at $2,190 a month is about $3.00 per GPU-hour if it is busy around the clock, but about $14.60 per used hour if it runs only 150 hours. If your GPUs would sit idle most of the month, hourly billing elsewhere will cost less; monthly wins when a GPU is busy most days. Plug in your own numbers with our GPU cost calculator, or read monthly vs hourly GPU pricing. When usage settles, ask for term pricing on 6–12 months or several servers, usually from three.

What we run and what you run

A dedicated server gives you full control, and with it some responsibility. Be clear on the split before you order:

• We handle the hardware and its replacement, power, network, DDoS mitigation on public IPs, the 99.9% availability SLA and 24/7 incident support.
• You handle the operating system after delivery, users and SSH keys, the firewall, your containers and frameworks, monitoring, and backups, since local storage is not backed up unless agreed in writing.

For a small team, three habits cover most of it: keep Dockerfiles and setup scripts in a repository so any new server can be rebuilt from one script, push checkpoints and datasets to storage you control, and watch GPU health with nvidia-smi or NVIDIA DCGM, as in our GPU monitoring guide.

Paying by card or crypto

We accept Visa, Mastercard and American Express, and crypto: Bitcoin on-chain and over Lightning, Ethereum, USDT on TRON and Ethereum, USDC on Ethereum, Base and Solana, and Litecoin. Prices are in USD, the exchange rate is locked at checkout, and you receive the same invoice whichever method you use, which helps teams that hold part of their treasury in stablecoins. Details are on our pay with crypto page. Most orders need only an email address and a payment; verification may be requested for large orders or for fraud and sanctions screening.

How to get started

1. Describe your workload, model sizes and timeline through the contact form.
2. We confirm the configuration, price, location and delivery date in writing. If we miss the date, you can cancel for a full refund.
3. Pay the first month through the payment link, by card or crypto.
4. Receive SSH credentials by email and start from Ubuntu with NVIDIA drivers, CUDA, Docker and the NVIDIA Container Toolkit preinstalled.

Our engineers provide 24/7 support, reached through the same contact form.