Professional Ada Lovelace GPU for AI rendering 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 5880 Ada 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 5880 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
|
2
|
–
|
–
|
GLM-5.3-Flash
320B · 18B active
|
178 GB
|
5
|
–
|
–
|
MiniMax-M3
428B · 23B active
|
239 GB
|
6
|
–
|
–
|
DeepSeek V4.1 Flash
552B · 16B active
|
306 GB
|
8
|
–
|
–
|
Kimi K3
2.8T · 104B active
|
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. Pricing methodology.
Nvidia RTX 5880 Ada · Per GPU
| Compute · dense | |
|---|---|
| FP8 | 554.2 TFLOPS 1,108.4 with sparsity |
| FP16 / BF16 | 277.1 TFLOPS 554.2 with sparsity |
| FP32 | 69.3 TFLOPS |
| Precision support | FP8FP16BF16TF32FP32INT4INT8 |
| Memory | |
|---|---|
| Capacity | 48 GB GDDR6 |
| Bandwidth | 960 GB/s |
| Bus width | 384-bit |
| ECC | Yes |
| Silicon | |
|---|---|
| Architecture | Ada Lovelace |
| Process | TSMC 4N |
| Transistors | 76.3 billion |
| Shader cores | 14,080 CUDA cores |
| Matrix cores | 440 Tensor cores |
| Compute units | 110 SMs |
| Fabric and host | |
|---|---|
| Host interface | PCIe 4.0 x16 |
| Power | |
|---|---|
| Board power | 285 W |
| Cooling | Active |
The cheapest verified in-stock estimate is $482 per month on-demand, $288 spot.
GetDeploying currently tracks RTX 5880 Ada configs from 1 provider. Vast.ai has verified in-stock on-demand configs. See the full price comparison above for every provider and config.
One RTX 5880 Ada 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 GDDR6 with Ada Lovelace FP8 Tensor Cores. Professional workstation GPU with ECC memory. Good balance of AI and visualization capabilities.
Workstation pricing. For pure AI workloads in the cloud, the L40S is more cost-effective with equivalent specs.
Nvidia RTX A6000
48 GB GDDR6 · 20% less bandwidth than the RTX 5880 Ada
Last updated
