Rent an NVIDIA RTX A6000 GPU with 48 GB VRAM
Rent RTX A6000 cloud GPU servers by the hour on Cloudj: 48 GB VRAM, Ampere architecture and about 36.1 TFLOPS FP32 with 651 GB/s of memory bandwidth. It is the classic 48 GB workstation GPU for AI developers and 3D studios who need big models and scenes on one card at a 300 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.
RTX A6000 specifications
VRAM per GPU
48 GB
Architecture
NVIDIA Ampere
Compute capability
8.6
FP32 compute
≈ 36.1 TFLOPS
Memory bandwidth
≈ 651 GB/s
Power limit
300 W
Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.
Live RTX A6000 configurations and prices
From $0.50/h
incl. 32 GB disk · Live price, updated every 15 minutes
What you can run on the RTX A6000 cloud GPU
70B LLMs at 4-bit
48 GB fits a 4-bit 70B model for evaluation and light serving, or a 30B-class model at 8-bit.
Fine-tuning 13B–34B models
Run LoRA on 13B models in BF16 or QLoRA on 30B-class models with Unsloth on a single GPU.
Large render scenes
Keep heavy production scenes and textures in 48 GB of VRAM for CUDA renderers.
Full-precision image models
Run Flux in 16-bit and multi-model ComfyUI graphs without memory juggling.
Is the RTX A6000 the right GPU for your workload?
Choose the RTX A6000 when your workloads exceed the memory limits of consumer cards and require 48 GB of VRAM for larger models or batch sizes. Opt for a smaller consumer GPU for lightweight inference, or step up to a newer architecture or datacenter GPU if your workflow requires higher memory bandwidth than about 651 GB/s.
RTX A6000 vs similar cloud GPUs
Guides for RTX A6000
RTX A6000 cloud GPU: frequently asked questions
Rent the RTX A6000 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.