Runpod
Our sponsor- On-Demand
- from $0.82
- Reserved
- on request
Blackwell professional GPU for AI development and visualization.
Weekly median price per GPU per hour
By provider shows one card per company, placed where its best offer ranked. By configuration lists every offer.
What you can rent comes first: in stock, then waitlist, then not reported, then out of stock. Priced offers rank ahead of quote-only, and on-demand ahead of other billing types.
Within a group, five factors set the order:
We match the provider, its country, GPU model, form factor, billing type, availability and instance name. Matching is partial and case-insensitive. Everyday words work too, so "interruptible" finds spot and "sold out" finds out of stock.
No providers match .
Heads up: A provider's own page may quote a different figure, for example on tax, region or a promotion. A monthly-only plan shows a derived hourly rate. Verify before provisioning. More on how we price.
One RTX PRO 5000 has 48 GB of VRAM. In practice, that's enough memory for roughly 68B parameters at 4-bit or 17B at 16-bit, assuming a 32K context. Below are some open-weight LLMs, with the estimated memory and GPUs each one needs.
| Model | Memory (INT4 / FP4) | RTX PRO 5000s needed | Cost /hr | Cost /mo |
|---|---|---|---|---|
|
|
18 GB
|
1
|
–
|
–
|
|
|
22 GB
|
1
|
–
|
–
|
|
|
67 GB
|
2
|
–
|
–
|
|
|
178 GB
|
8
|
–
|
–
|
|
|
239 GB
|
8
|
–
|
–
|
|
|
306 GB
|
8
|
–
|
–
|
|
|
1,544 GB
|
36
|
–
|
–
|
Estimates based on the median on-demand rate. Memory is weights plus FP8 KV cache at 32K context per request (FP16/BF16 in the 16-bit column). GPU counts assume 90% of advertised VRAM is usable. No guarantee of runtime support, usable performance, or that a matching quantized build exists. How we estimate costs.
Nvidia RTX PRO 5000 · Per GPU
| Compute · dense | |
|---|---|
| FP4 | 1,032 TFLOPS 2,064 with sparsity |
| FP8 | 516 TFLOPS 1,032 with sparsity |
| FP16 / BF16 | 258 TFLOPS 516 with sparsity |
| INT8 | 516 TOPS 1,032 with sparsity |
| FP32 | 65 TFLOPS |
| Precision support | FP4FP8FP16BF16TF32FP32INT8 |
| Memory | |
|---|---|
| Capacity | 48 GB GDDR7 |
| Bandwidth | 1,344 GB/s |
| Bus width | 512-bit |
| ECC | Yes |
| Silicon | |
|---|---|
| Architecture | Blackwell |
| Process | TSMC 4N |
| Transistors | 92.2 billion |
| Shader cores | 14,080 CUDA cores |
| Matrix cores | 440 Tensor cores |
| Compute units | 110 SMs |
| Fabric and host | |
|---|---|
| Host interface | PCIe 5.0 x16 |
| Power | |
|---|---|
| Board power | 300 W |
| Cooling | Active |
As of September 18, 2026, we track 32 configs from 4 providers. Prices are per GPU per hour.
| Billing type | Configs | Median /GPU/hr | Cheapest |
|---|---|---|---|
On-demand | 13 | $0.70 (in stock, Vast.ai) |
|
Reserved | 10 | $0.83 | $0.50 (24 mo) |
Spot | 9 | $0.17 (in stock, Vast.ai) |
No median for on-demand and spot: we only show this when at least 3 providers list the GPU on that billing type.
The cheapest verified in-stock estimate is $504 per month on-demand, $454 reserved, $122 spot.
GetDeploying currently tracks RTX PRO 5000 configs from 4 providers. Vast.ai and Runpod have verified in-stock on-demand configs. See the full price comparison above for every provider and config.
One RTX PRO 5000 runs models up to roughly 68B parameters at 4-bit quantization or 17B at 16-bit, assuming a 32K context. Larger models run across multiple GPUs: the model table above shows the estimated memory and GPU count for popular open-weight LLMs.
48GB GDDR7 with Blackwell FP4 Tensor Cores. Professional desktop GPU with ECC memory for AI development and visualization.
Workstation GPU with limited cloud availability. For cloud inference, data center GPUs offer better support and pricing.
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