Rent an NVIDIA Q RTX 8000 GPU with 48 GB VRAM
Rent Q RTX 8000 cloud GPU servers by the hour on Cloudj: 48 GB VRAM on NVIDIA Turing with about 11.9 TFLOPS FP32 and 434 GB/s of memory bandwidth. It is a big-memory workstation card for artists and researchers whose scenes, datasets or quantized models do not fit in 24 GB, at a 250 W power limit. Pick a live configuration, start from the Ubuntu 22.04 + CUDA or PyTorch + JupyterLab template and connect over SSH with your key.
Q RTX 8000 specifications
VRAM per GPU
48 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.
Live Q RTX 8000 configurations and prices
From $0.32/h
incl. 32 GB disk · Live price, updated every 15 minutes
What you can run on the Q RTX 8000 cloud GPU
Huge rendering scenes
48 GB keeps large scenes, textures and simulation caches on the GPU instead of falling back to the CPU.
Quantized 30B-class LLMs
Load a 4-bit 30B–34B model in Ollama with room for a long context window.
High-resolution image work
Run SDXL at high resolution with multiple ControlNets in ComfyUI without running out of memory.
Large-memory data experiments
Keep big point clouds, volumes or embeddings resident in GPU memory for analysis.
Is the Q RTX 8000 the right GPU for your workload?
Choose the Q RTX 8000 when your primary bottleneck is VRAM capacity and you need 48 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.
Q RTX 8000 vs similar cloud GPUs
Guides for Q RTX 8000
Q RTX 8000 cloud GPU: frequently asked questions
Rent the Q RTX 8000 cloud GPU by the hour
Pick a live configuration, choose a template and connect over SSH with your key. Billing stops when you destroy the instance.