Mid-range Ada Lovelace professional GPU for AI 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.
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 5000 Ada has 32 GB of VRAM. In practice, that's enough memory for roughly 41B parameters at 4-bit or 9B 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 5000 Adas 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
|
3
|
–
|
–
|
GLM-5.3-Flash
320B · 18B active
|
178 GB
|
7
|
–
|
–
|
MiniMax-M3
428B · 23B active
|
239 GB
|
9
|
–
|
–
|
DeepSeek V4.1 Flash
552B · 16B active
|
306 GB
|
11
|
–
|
–
|
Kimi K3
2.8T · 104B active
|
1,544 GB
|
54
|
–
|
–
|
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 5000 Ada · Per GPU
| Compute · dense | |
|---|---|
| FP8 | 522.2 TFLOPS 1,044.4 with sparsity |
| FP16 / BF16 | 261.1 TFLOPS 522.2 with sparsity |
| FP32 | 65.3 TFLOPS |
| Precision support | FP8FP16BF16TF32FP32INT4INT8 |
| Memory | |
|---|---|
| Capacity | 32 GB GDDR6 |
| Bandwidth | 576 GB/s |
| Bus width | 256-bit |
| ECC | Yes |
| Silicon | |
|---|---|
| Architecture | Ada Lovelace |
| Process | TSMC 4N |
| Transistors | 76.3 billion |
| Shader cores | 12,800 CUDA cores |
| Matrix cores | 400 Tensor cores |
| Compute units | 100 SMs |
| Fabric and host | |
|---|---|
| Host interface | PCIe 4.0 x16 |
| Power | |
|---|---|
| Board power | 250 W |
| Cooling | Active |
As of September 13, 2026, we track 12 configs from 2 providers. Prices are per GPU per hour.
| Billing type | Configs | Cheapest |
|---|---|---|
On-demand | 6 | $0.34 (in stock, Vast.ai) |
Reserved | 2 | $0.57 (3 mo, in stock, Vast.ai) |
Spot | 4 | $0.31 (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 $245 per month on-demand, $410 reserved (3 mo), $223 spot.
GetDeploying currently tracks RTX 5000 Ada configs from 2 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 5000 Ada runs models up to roughly 41B parameters at 4-bit quantization or 9B 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.
32GB GDDR6 with Ada Lovelace FP8 support. Professional GPU with ECC memory. Suitable for AI development and mid-range visualization.
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