Rent an NVIDIA A40 GPU with 48 GB VRAM
Rent A40 cloud GPU servers by the hour on Cloudj: 48 GB VRAM, Ampere architecture and about 29.9 TFLOPS FP32 for rendering, image generation and AI inference. The A40 suits 3D artists and AI developers who need a large frame buffer more than raw memory bandwidth (536 GB/s, 300 W). Pick a live configuration, start from the Ubuntu 22.04 + CUDA or PyTorch + JupyterLab template and connect over SSH with your key.
A40 specifications
VRAM per GPU
48 GB
Architecture
NVIDIA Ampere
Compute capability
8.6
FP32 compute
≈ 29.9 TFLOPS
Memory bandwidth
≈ 536 GB/s
Power limit
300 W
Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.
Live A40 configurations and prices
From $0.59/h
incl. 32 GB disk · Live price, updated every 15 minutes
What you can run on the A40 cloud GPU
Large-scene GPU rendering
48 GB of VRAM keeps heavy geometry and high-resolution textures on the GPU, and 29.9 TFLOPS FP32 drives CUDA path tracers.
Flux and SDXL batch generation
Run ComfyUI workflows with Flux or SDXL plus ControlNet and upscalers loaded at once, without swapping models in and out.
Quantized 30B-class LLMs
Load a 4-bit or 8-bit 30B–34B model in Ollama or vLLM for private chat, agents or evaluation runs.
Headless rendering
Render large Blender scenes in batch mode over SSH, with the whole scene held in GPU memory.
Is the A40 the right GPU for your workload?
Choose the A40 when your workload requires 48 GB of VRAM on a datacenter Ampere architecture, which exceeds standard consumer memory capacities. Opt for a smaller consumer card if your models fit into less memory, or move to a higher-bandwidth datacenter GPU if your training jobs are heavily bottlenecked by memory throughput.