Turing professional GPU for legacy visualization workloads.
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 has 16 GB of VRAM. In practice, that's enough memory for roughly 8B parameters at 4-bit or 2B 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 5000s needed | Cost /hr | Cost /mo |
|---|---|---|---|---|
Qwen3.8-27B
27B
|
19 GB
|
2
|
–
|
–
|
Gemma 4 31B
30.7B
|
24 GB
|
2
|
–
|
–
|
GPT-OSS-120B
117B · 5.1B active
|
68 GB
|
5
|
–
|
–
|
GLM-5.3-Flash
320B · 18B active
|
178 GB
|
13
|
–
|
–
|
MiniMax-M3
428B · 23B active
|
241 GB
|
17
|
–
|
–
|
DeepSeek V4.1 Flash
552B · 16B active
|
306 GB
|
22
|
–
|
–
|
Kimi K3
2.8T · 104B active
|
1,545 GB
|
108
|
–
|
–
|
Estimates based on the median on-demand rate. Memory is weights plus FP16/BF16 KV cache at 32K context per request. 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 · Per GPU
| Compute · dense | |
|---|---|
| FP16 / BF16 | 89.2 TFLOPS |
| INT8 | 178.4 TOPS |
| FP32 | 11.2 TFLOPS |
| Precision support | FP16FP32INT4INT8 |
| Memory | |
|---|---|
| Capacity | 16 GB GDDR6 |
| Bandwidth | 448 GB/s |
| Bus width | 256-bit |
| ECC | Yes |
| Silicon | |
|---|---|
| Architecture | Turing |
| Process | TSMC 12nm FFN |
| Transistors | 13.6 billion |
| Shader cores | 3,072 CUDA cores |
| Matrix cores | 384 Tensor cores |
| Compute units | 48 SMs |
| Fabric and host | |
|---|---|
| GPU interconnect | NVLink 50 GB/s |
| Host interface | PCIe 3.0 x16 |
| Power | |
|---|---|
| Board power | 265 W |
| Cooling | Active |
Source: official Nvidia RTX 5000 datasheet.
As of October 24, 2026, we track 4 configs from 2 providers. Prices are per GPU per hour.
| Billing type | Configs | Cheapest |
|---|---|---|
On-demand | 4 | $0.60 (not verified in stock, OVHcloud) |
No median for on-demand: we only show this when at least 3 providers list the GPU on that billing type.
The lowest listed on-demand price for the RTX 5000 is $0.60 per GPU per hour from OVHcloud, though we haven't verified current stock.
GetDeploying currently tracks RTX 5000 configs from 2 providers. See the full price comparison above for every provider and config.
One RTX 5000 runs models up to roughly 8B parameters at 4-bit quantization or 2B 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.
16GB GDDR6 with NVLink (112 GB/s). Turing professional GPU for visualization and moderate compute.
16GB VRAM and Turing architecture are limiting. For AI work, newer Ada Lovelace professional GPUs are a much better choice.
Nvidia A5000
24 GB GDDR6 · 71% more bandwidth than the RTX 5000
Nvidia Quadro RTX 6000
24 GB GDDR6 · 50% more bandwidth than the RTX 5000
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