Entry-level Blackwell professional GPU for AI prototyping.
Weekly median price per GPU per hour · Get the data
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 4000 has 24 GB of VRAM. In practice, that's enough memory for roughly 28B parameters at 4-bit or 6B 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 4000s needed | Cost /hr | Cost /mo |
|---|---|---|---|---|
Qwen3.8-27B
27B
|
18 GB
|
1
|
–
|
–
|
Gemma 4 31B
30.7B
|
22 GB
|
1
|
–
|
–
|
GPT-OSS-120B
117B · 5.1B active
|
67 GB
|
8
|
–
|
–
|
GLM-5.3-Flash
320B · 18B active
|
178 GB
|
9
|
–
|
–
|
MiniMax-M3
428B · 23B active
|
239 GB
|
12
|
–
|
–
|
DeepSeek V4.1 Flash
552B · 16B active
|
306 GB
|
15
|
–
|
–
|
Kimi K3
2.8T · 104B active
|
1,544 GB
|
72
|
–
|
–
|
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. Pricing methodology.
Nvidia RTX PRO 4000 · Per GPU
| Compute · dense | |
|---|---|
| FP4 | 589 TFLOPS 1,178 with sparsity |
| FP8 | 294.5 TFLOPS 589 with sparsity |
| FP16 / BF16 | 147.3 TFLOPS 294.5 with sparsity |
| INT8 | 294.5 TOPS 589 with sparsity |
| FP32 | 37 TFLOPS |
| Precision support | FP4FP8FP16BF16TF32FP32INT8 |
| Memory | |
|---|---|
| Capacity | 24 GB GDDR7 |
| Bandwidth | 672 GB/s |
| Bus width | 192-bit |
| ECC | Yes |
| Silicon | |
|---|---|
| Architecture | Blackwell |
| Process | TSMC 4N |
| Transistors | 45.6 billion |
| Shader cores | 8,960 CUDA cores |
| Matrix cores | 280 Tensor cores |
| Compute units | 70 SMs |
| Fabric and host | |
|---|---|
| Host interface | PCIe 5.0 x16 |
| Power | |
|---|---|
| Board power | 145 W |
| Cooling | Active |
As of October 3, 2026, we track 46 configs from 3 providers. Prices are per GPU per hour.
| Billing type | Configs | Cheapest |
|---|---|---|
On-demand | 22 | $0.20 (in stock, Vast.ai) |
Reserved | 8 | $0.26 (24 mo) |
Spot | 16 | $0.14 (in stock, Vast.ai) |
No median for on-demand, reserved 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 $144 per month on-demand, $194 reserved, $101 spot.
GetDeploying currently tracks RTX PRO 4000 configs from 3 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 4000 runs models up to roughly 28B parameters at 4-bit quantization or 6B 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.
24GB GDDR7 with Blackwell FP4 Tensor Cores. Entry-level Blackwell professional GPU for AI prototyping and visualization.
Limited VRAM for larger models. Desktop form factor with minimal cloud availability.
Nvidia A4000
16 GB GDDR6 · 33% less bandwidth than the RTX PRO 4000
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