--- title: "Rent Q RTX 8000 — $0.178/hr Cloud GPU Pricing (2026) | PowerGPU" description: "Rent the Q RTX 8000 (48 GB GDDR6) from $0.089/hr interruptible or $0.178/hr on-demand — fixed, ≥30% below market. 5 GPUs in 2 regions, per-second billing." url: https://powergpu.ai/gpu/q-rtx-8000 last_modified: 2026-09-14T11:04:20+00:00 prices_as_of: 2026-09-14 site: PowerGPU (powergpu.ai) --- Previous gen · Turing · launched 2018 · prices checked 2026-09-14 # Rent NVIDIA Q RTX 8000 — 48 GB, $0.178/hr on-demand - VRAM 48 GB GDDR6 - FP16 tensor 65 TFLOPS - PowerScore 46 RTX 3090 = 100 - Configs 1–2× PCIe 3.0 - Online now 5 2 regions [On-demand (guaranteed) $0.178 /GPU-hr ≈ $130/mo · market ~~$0.25~~ (−30%)](https://cloud.powergpu.ai/?gpu=q-rtx-8000) [Interruptible $0.089 /GPU-hr flat −50% · pausable, disk kept](https://cloud.powergpu.ai/?gpu=q-rtx-8000&type=spot) [Reserved 3 mo $0.115 /GPU-hr −35% · capacity held for you](https://powergpu.ai/products/reserved) Billed per second, price locked at deploy. Storage $0.08/GB/mo · bandwidth $0.01/GB — [the whole fee schedule](https://powergpu.ai/pricing). Next weekly market re-check: 2026-09-21. Previous gen · Turing architecture The Quadro RTX 8000 is a Turing workstation card with 48 GB of GDDR6 and NVLink bridge support. Slow by modern tensor standards, but 48 GB at this price is useful for VRAM-bound rendering and for running 32B-class 4-bit models on a budget. 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 8000 specs: VRAM, TFLOPS, bandwidth - **GPU model**: NVIDIA Q RTX 8000 · **Architecture**: Turing (2018) - **VRAM**: 48 GB GDDR6 · **Memory bandwidth**: 672 GB/s - **FP16 tensor perf.**: 65 TFLOPS · **FP32 perf.**: 16.3 TFLOPS - **CUDA cores**: 4,608 · **TDP**: 295 W - **PowerScore ((RTX 3090 = 100))**: 46 · **PCIe generation**: Gen 3.0 - **Multi-GPU**: 1× – 2× · **Max instance storage**: 8,000 GB NVMe - **Network up to**: 10,000 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 8000 price per hour: on-demand, interruptible, reserved One public rule sets every price on this page: the marketplace median for the Q RTX 8000 ($0.25/hr, snapshot 2026-09-14) × 0.70, rounded down — so on-demand is $0.178, **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** | $0.178 | $4.27 | $130 | Guaranteed capacity, price locked at deploy, stop anytime | | **Interruptible** | $0.089 | $2.14 | $65 | Flat −50%; may pause under capacity pressure, disk kept, auto-requeue | | **Reserved (3 months)** | $0.115 | $2.76 | $84 | −35% on on-demand, rate locked for the term, capacity held | Per GPU: an 2× machine costs exactly 2× — no multi-GPU premium. Estimate a full month with storage and bandwidth in the [GPU cost calculator](https://powergpu.ai/calculator). ## What you can run on a Q RTX 8000 (48 GB VRAM) With 48 GB of GDDR6, a single card holds a **~14B-parameter LLM in FP16** or up to **~72B parameters quantized to 4-bit**, with room for KV-cache at practical context lengths. Scale to 2× GPUs on one machine for bigger models or bigger batches — the per-GPU price stays $0.178. - One-click template: [Ollama on a Q RTX 8000](https://powergpu.ai/templates/ollama) - One-click template: [SD WebUI Forge on a Q RTX 8000](https://powergpu.ai/templates/sd-webui-forge) - One-click template: [Whisper WebUI & API on a Q RTX 8000](https://powergpu.ai/templates/whisper-webui-api) - Sizing help: [LLM VRAM requirements guide](https://powergpu.ai/guides/llm-vram-requirements) ## Q RTX 8000 availability by region 5 × Q RTX 8000 across 2 machines, live from inventory: - Santiago - Bucharest ## Q RTX 8000 vs alternatives: price per TFLOP | GPU | VRAM | FP16 | On-demand | $ / TFLOP-hr | | --- | --- | --- | --- | --- | | **Q RTX 8000** (this card) | 48 GB | 65 | $0.178 | $2.74‰ | | [RTX PRO 4000](https://powergpu.ai/gpu/rtx-pro-4000) | 20 GB | 150 | $0.183 | $1.22‰ | | [RTX A5000](https://powergpu.ai/gpu/rtx-a5000) | 24 GB | 111 | $0.161 | $1.45‰ | | [RTX 4000Ada](https://powergpu.ai/gpu/rtx-4000ada) | 20 GB | 107 | $0.128 | $1.20‰ | | [Titan RTX](https://powergpu.ai/gpu/titan-rtx) | 24 GB | 65 | $0.103 | $1.58‰ | ‰ = dollars per 1,000 TFLOP-hours of FP16 — a rough value-for-compute yardstick across cards. ## Renting a Q RTX 8000: frequently asked questions **How much does it cost to rent an NVIDIA Q RTX 8000 per hour?** $0.178 per GPU-hour on-demand — a fixed price set at least 30% below the current market median of $0.25. Interruptible capacity costs $0.089/hr and a 3-month reservation $0.115/hr. Around $130/month if you keep one running non-stop, billed per second. **What can a Q RTX 8000 with 48 GB VRAM run?** In LLM terms, roughly a 14B-parameter model in FP16 or up to ~72B parameters 4-bit quantized on a single card, with context headroom. Multi-GPU instances (up to 2× on current inventory) multiply that; diffusion and rendering workloads fit comfortably at this VRAM class. **Is the Q RTX 8000 available to rent right now?** Yes — 5 GPUs across 2 machines in 2 regions are listed as we render this page. Configurations go from 1× to 2×. Deploy from the console and it is running in about 30 seconds. **How do I deploy a Q RTX 8000?** Create an account (email + password, no card, no KYC), top up in crypto, open the console, filter by Q RTX 8000, pick a machine and a template such as PyTorch, vLLM or ComfyUI. The same deploy is one command with the CLI: powergpu launch --gpu q-rtx-8000 --template pytorch. **Why is the Q RTX 8000 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 2026-09-21) and published — no auctions, no per-host roulette, no bidding. --- *About PowerGPU:* PowerGPU (powergpu.ai) is a cloud GPU rental service offering 80 NVIDIA GPU models — from the RTX A2000 at $0.024/hr to the B300 — at fixed prices set at least 30% below the public GPU marketplace median and re-checked weekly (H100 SXM: $1.428/hr on-demand). Billing is per second with no minimums; payment is crypto only (USDT, BTC, XMR, ETH, SOL, LTC, TRX) with no KYC. Instances run in Tier-III datacenters across 32 regions with a 99.9% uptime SLA and deploy in about 30 seconds from the web console (cloud.powergpu.ai) or the REST API. Source: https://powergpu.ai/gpu/q-rtx-8000 · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt