Rent a dedicated RTX 5090 cloud server for $449 per month

If you want to rent RTX 5090 power without buying and hosting the card yourself, we offer one whole GeForce RTX 5090 with 32 GB of GDDR7 in a dedicated virtual machine for $449 per month. It is our lowest-priced plan and a good fit for image generation, small or quantized language models, prototyping and development, as long as your work fits in 32 GB. Below, we show exactly where that limit bites.

What the RTX 5090 is, and what it is not

The RTX 5090 is NVIDIA's Blackwell GeForce flagship. Per NVIDIA's specifications, it has 32 GB of GDDR7 at 1.79 TB/s, connects over PCIe Gen5 and draws up to 575 W, and its Tensor Cores support low-precision formats down to FP4, which NVIDIA lists at 3,352 AI TOPS with sparsity. For models that fit in memory, that bandwidth is high for the price: more than the L40S (864 GB/s) or the RTX PRO 6000 (1.6 TB/s).

It is still a GeForce card designed for desktops, not a data-center GPU. It has no NVLink, its memory stops at 32 GB, and we rent it as a single-GPU plan only. Treat it as a fast, affordable GPU for work that fits, not as a substitute for an H100 in production. If you are upgrading from an older card, our page on how it compares with the previous GeForce generation covers the differences, and RTX 5090 vs RTX PRO 6000 shows when to step up.

What fits in 32 GB, and what does not

Weights take roughly 2 bytes per parameter in BF16, 1 in FP8 and 0.5 in 4-bit, plus KV cache or activations and 10–30% headroom.

• Image generation: SDXL, with about 3.5B parameters, takes around 7 GB in FP16 and leaves room for large batches. A 12B-parameter model such as FLUX.1 needs about 24 GB for its BF16 weights alone, so on 32 GB you run it in FP8 (about 12 GB) or let the pipeline unload the text encoder once the prompt is encoded.
• Video generation: a 14B-parameter video model needs about 28 GB in BF16 before activations, which does not leave enough room. Use FP8 weights, a smaller model variant, or a 96 GB RTX PRO 6000.
• LLM inference: an 8B model in BF16 (about 16 GB) leaves roughly 10 GB for KV cache after headroom; a 14B model fits in FP8 (about 14 GB) but not in BF16; a 32B model fits in 4-bit (about 16 GB). A 70B model does not fit, even in 4-bit (about 35 GB). For scale, a Llama-style 8B model with grouped-query attention (32 layers, 8 KV heads) needs about 0.13 MB of KV cache per token in BF16, so 10 GB holds roughly 75,000 tokens across all active requests.
• Training: LoRA on image models and QLoRA on 8B–14B LLMs work. Full fine-tuning with Adam needs roughly 16–20 bytes per parameter, which limits it to models of around 1B parameters.

The plan's 64 GB of system RAM also limits how much you can offload to the CPU, so treat 32 GB as a firm ceiling. When you reach it, move video and large image pipelines to the RTX PRO 6000 and LLM serving to the L40S or H100; our generative media guide covers sizing for media workloads.

A development box with the same CUDA stack

An RTX 5090 also works well as a persistent development machine. You get root access over SSH, Ubuntu 22.04 or 24.04 with NVIDIA drivers, CUDA, Docker and the NVIDIA Container Toolkit preinstalled, and 1 TB of NVMe where downloaded models and datasets stay between sessions. Code written here runs on our H100 and H200 servers with the same CUDA tooling, as long as any compiled extensions target both architectures, so you can prototype at $449 per month and move to a data-center GPU when the workload grows.

One practical point: Blackwell needs recent software. Install PyTorch builds for CUDA 12.8 or later; older builds do not include kernels for this GPU and stop with a "no kernel image is available" error. A quick check after setup:

nvidia-smi        # GPU model, driver and CUDA version
python -c "import torch; print(torch.cuda.get_device_name(0),
  torch.version.cuda)"

Editors that work over SSH connect directly, and web tools such as JupyterLab or ComfyUI are best reached through an SSH tunnel rather than a public port. Our guides to NVIDIA drivers and CUDA and to remote development go further.

RTX 5090 price per month and per GPU-hour

The RTX 5090 costs $449 per month, about $0.62 per GPU-hour if it runs all 730 hours. That suits an always-on generation queue or a small endpoint serving traffic around the clock. For a development box, it depends on how you count: used 8 hours a day on 22 working days (176 hours), it costs about $2.55 per used hour, in exchange for an environment that is always ready. For a single weekend of work, hourly billing elsewhere usually costs less. To find your break-even point, divide $449 by the hourly rate you would pay.

The price includes 20 TB of outbound traffic per month with no egress fees within that allowance, a public IPv4 address and an IPv6 /64, DDoS mitigation, 24/7 support from engineers and the 99.9% availability SLA, with no setup fee. Your environment stays as you left it for as long as you keep the plan.

Ordering an RTX 5090

Deploy an RTX 5090 from one form: choose the configuration, set up the OS image, optional preinstalled software and your login (root password or SSH key), create a free account or log in, and pay the first month from your prepaid balance, which you top up in BTC, ETH, XMR, USDT on TRON or SOL (how crypto payment works). If your balance is short, you top up in the same form; the order is held for 3 hours and paid automatically as soon as the top-up is credited. Card payments are coming soon. Most orders need only an account and a funded balance. The server is delivered in under 10 minutes after payment, with the IP and SSH details in your dashboard. You can cancel any month with no fee. Once you have an active server, you can ask for volume pricing on several 5090s for a team or a class in a ticket from your dashboard, giving the quantity and duration.