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 Cloudj?

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?

Cloudj 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.

GPU Cloud · NVIDIA · Turing

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 Cloudj with per-hour billing, full SSH access, and pre-configured templates.

Rent Q RTX 8000 All GPUs

Specifications

VRAM per GPU

45 GB

Architecture

NVIDIA Turing

Compute capability

7.5

FP32 compute

≈ 11.9 TFLOPS

Memory bandwidth

≈ 434 GB/s

Power limit

250 W

Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.

Available configurations

Live stock and prices, updated continuously. The final price is confirmed before you order.

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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

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA Q RTX 8000	45 GB	≈ 11.9 TFLOPS	250 W
NVIDIA RTX A6000	48 GB	≈ 36.1 TFLOPS	300 W	Details →
NVIDIA RTX PRO 5000	48 GB	≈ 69.6 TFLOPS	300 W	Details →
NVIDIA RTX 6000Ada	48 GB	≈ 81.4 TFLOPS	300 W	Details →
NVIDIA RTX 4500Ada	24 GB	≈ 38.2 TFLOPS	210 W	Details →

Frequently asked questions

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 All GPUs