Mid-range Ada Lovelace professional GPU for AI and visualization.
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 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. Pricing methodology.
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 October 3, 2026, we track 6 configs from 2 providers. Prices are per GPU per hour.
| Billing type | Configs | Cheapest |
|---|---|---|
On-demand | 3 | $0.34 (in stock, Vast.ai) |
Reserved | 3 | $0.30 (3 mo, in stock, Vast.ai) |
No median for on-demand and reserved: 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, $216 reserved (3 mo).
GetDeploying currently tracks RTX 5000 Ada configs from 2 providers. Vast.ai has 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.
Nvidia L40S
48 GB GDDR6 · 50% more bandwidth than the RTX 5000 Ada
Nvidia A5000
24 GB GDDR6 · 33% more bandwidth than the RTX 5000 Ada
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