Previous gen · Turing · prices checked 2026-10-19
Rent NVIDIA Q RTX 5000 — 16 GB, $0.074/hr on-demand
- VRAM 16 GBGDDR6
- FP16 tensor 48TFLOPS
- PowerScore 34RTX 3090 = 100
- Configs 1–1×PCIe 3.0
- Online now 7 0 regions
Billed per second, price locked at deploy. Storage $0.08/GB/mo · bandwidth $0.01/GB — the whole fee schedule. Next weekly market re-check: 2026-10-26.
Previous gen · Turing architecture
A cost-efficient card for right-sized jobs: batch inference, smaller models, CI pipelines and experiments where a flagship would idle. Per-second billing makes it perfect for short bursts.
NVIDIA Q RTX 5000 specs: VRAM, TFLOPS, bandwidth
| GPU model | NVIDIA Q RTX 5000 | Architecture | Turing |
|---|---|---|---|
| VRAM | 16 GB GDDR6 | Memory bandwidth | — |
| FP16 tensor perf. | 48 TFLOPS | FP32 perf. | — |
| CUDA cores | — | TDP | — |
| PowerScore (RTX 3090 = 100) | 34 | PCIe generation | Gen 3.0 |
| Multi-GPU | 1× – 1× | Max instance storage | 0 GB NVMe |
| Network up to | 0 Mbps | CUDA | 12.4 – 13.0 |
Bandwidth, CUDA cores, TDP and FP32 are public NVIDIA figures; FP16 tensor is the dense (non-sparsity) number. Machine-level values come from live inventory.
Q RTX 5000 price per hour: on-demand, interruptible, reserved
One public rule sets every price on this page: the marketplace median for the Q RTX 5000 ($0.11/hr, snapshot 2026-10-19) × 0.70, rounded down — so on-demand is $0.074, 30% below market. Interruptible halves it; a 3-month reservation takes another 35% off.
| Mode | Per GPU-hour | Per day (24 h) | Per month (730 h) | What you get |
|---|---|---|---|---|
| On-demand | $0.074 | $1.78 | $54 | Guaranteed capacity, price locked at deploy, stop anytime |
| Interruptible | $0.037 | $0.89 | $27 | Flat −50%; may pause under capacity pressure, disk kept, auto-requeue |
| Reserved (3 months) | $0.048 | $1.15 | $35 | −35% on on-demand, rate locked for the term, capacity held |
Per GPU: an 1× machine costs exactly 1× — no multi-GPU premium. Estimate a full month with storage and bandwidth in the GPU cost calculator.
What you can run on a Q RTX 5000 (16 GB VRAM)
With 16 GB of GDDR6, a single card holds a ~3B-parameter LLM in FP16 or up to ~24B parameters quantized to 4-bit, with room for KV-cache at practical context lengths. Scale to 1× GPUs on one machine for bigger models or bigger batches — the per-GPU price stays $0.074.
- One-click template: Ollama on a Q RTX 5000
- One-click template: SD WebUI Forge on a Q RTX 5000
- One-click template: Whisper WebUI & API on a Q RTX 5000
- Sizing help: LLM VRAM requirements guide
Q RTX 5000 availability by region
7 × Q RTX 5000 across 0 machines, live from inventory:
Q RTX 5000 vs alternatives: price per TFLOP
| GPU | VRAM | FP16 | On-demand | $ / TFLOP-hr |
|---|---|---|---|---|
| Q RTX 5000 this card | 16 GB | 48 | $0.074 | $1.54‰ |
| RTX A4000 | 16 GB | 76 | $0.093 | $1.22‰ |
| Quadro P4000 | 8 GB | 8 | $0.038 | $4.75‰ |
| RTX A2000 | 6 GB | 32 | $0.036 | $1.13‰ |
| Titan RTX | 24 GB | 65 | $0.122 | $1.88‰ |
‰ = dollars per 1,000 TFLOP-hours of FP16 — a rough value-for-compute yardstick across cards.
Renting a Q RTX 5000: frequently asked questions
How much does it cost to rent an NVIDIA Q RTX 5000 per hour?
$0.074 per GPU-hour on-demand — a fixed price set at least 30% below the current market median of $0.11. Interruptible capacity costs $0.037/hr and a 3-month reservation $0.048/hr. Around $54/month if you keep one running non-stop, billed per second.
What can a Q RTX 5000 with 16 GB VRAM run?
In LLM terms, roughly a 3B-parameter model in FP16 or up to ~24B parameters 4-bit quantized on a single card, with context headroom. Multi-GPU instances (up to 1× on current inventory) multiply that; diffusion and rendering workloads fit comfortably at this VRAM class.
Is the Q RTX 5000 available to rent right now?
Yes — 7 GPUs across 0 machines in 0 regions are listed as we render this page. Configurations go from 1× to 1×. Deploy from the console and it is running in about 30 seconds.
How do I deploy a Q RTX 5000?
Create an account (email + password, no card, no KYC), top up in crypto, open the console, filter by Q RTX 5000, pick a machine and a template such as PyTorch, vLLM or ComfyUI. The same deploy is one command with the CLI: lowcostgpu launch --gpu q-rtx-5000 --template pytorch.
Why is the Q RTX 5000 cheaper here than on GPU marketplaces?
We price from the public marketplace median and fix our on-demand rate at least 30% below it, rounded down. The price is re-checked weekly (next check 2026-10-26) and published — no auctions, no per-host roulette, no bidding.