Rent NVIDIA Q RTX 8000 GPU by the hour
The NVIDIA Q RTX 8000 is a Turing-architecture workstation GPU featuring 45 GB of VRAM and a 250 W TDP. With CUDA compute capability 7.5, it delivers about 11.9 TFLOPS of standard floating-point compute and about 434 GB/s of memory bandwidth. Rent it on ONIE with per-hour billing, full SSH access, and pre-configured templates.
Specifications
VRAM per GPU
Architecture
Compute capability
FP32 compute
Memory bandwidth
Power limit
Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.
Available configurations
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What the Q RTX 8000 is good for
Large Language Model Inference
The 45 GB VRAM capacity allows loading substantial language models into memory using ready templates like vLLM and Ollama.
Memory-Intensive Model Fine-Tuning
Train and adapt deep learning models in PyTorch without running into out-of-memory errors on high-batch workloads.
Complex Image Generation Workflows
Run multi-stage ComfyUI pipelines that require holding multiple checkpoints and control nets in 45 GB of memory simultaneously.
Workstation Rendering and Simulation
Handle large scene files and high-resolution textures that benefit from Turing workstation hardware and ample video memory.
Is it the right GPU for you?
Choose the Q RTX 8000 when your primary bottleneck is VRAM capacity and you need 45 GB on a single card without paying for modern datacenter compute speeds. If your workload requires faster throughput than about 11.9 TFLOPS and 434 GB/s bandwidth, step up to a newer architecture, or pick a smaller consumer GPU if your models fit in less memory.
Compare with similar GPUs
Frequently asked questions
Is the Q RTX 8000 suitable for LLM inference and fine-tuning?
Yes, its 45 GB of VRAM makes it capable of holding large model weights and context windows that do not fit on standard consumer cards. However, because it is based on the Turing architecture with about 434 GB/s of memory bandwidth, token generation and training speeds will be slower than on newer datacenter GPUs.
What software compatibility does compute capability 7.5 offer?
Compute capability 7.5 is part of the NVIDIA Turing generation and is supported by standard deep learning frameworks via our PyTorch/CUDA, vLLM, Ollama, and ComfyUI templates. Please note that certain features introduced in newer architectures are not natively supported on Turing hardware.
How does this GPU compare to other options on ONIE?
As a workstation-class card, the Q RTX 8000 offers much more memory at 45 GB than typical consumer GPUs, though its compute performance of about 11.9 TFLOPS is modest compared to newer architectures. It serves as a practical middle ground when you need high memory capacity rather than maximum processing speed.
How do billing and access work when renting this GPU?
ONIE provides per-hour billing so you only pay for the time your instance is active. You can connect directly via SSH or launch immediately into pre-configured environments including PyTorch/CUDA, vLLM, Ollama, and ComfyUI.
Rent the Q RTX 8000 by the hour
Create an account, choose a configuration and connect over SSH. Billing stops when you delete the GPU.
Rent Q RTX 8000