Is the RTX A2000 suitable for LLM inference or fine-tuning?

With 6 GB of VRAM and about 11.2 TFLOPS of compute, the RTX A2000 is limited to inference on very small, quantized models using tools like Ollama. For fine-tuning or running larger language models, you should choose a GPU with significantly more VRAM.

Can I use this GPU for image generation?

Yes, you can run basic image generation workflows using the ComfyUI template, provided the models and resolutions are configured to stay within the 6 GB memory ceiling. Heavier diffusion pipelines and high-resolution generations require a GPU with a larger memory buffer.

How does the RTX A2000 compare to larger Ampere workstation GPUs?

The RTX A2000 sits at the entry point of the Ampere workstation line with 6 GB of VRAM and a 70 W thermal design power. Stepping up to higher-tier Ampere workstation models provides more VRAM and greater memory bandwidth than the about 247 GB/s offered here, while preserving CUDA compute capability 8.6 compatibility.

How do billing and access work on Cloudj?

Instances are billed on a per-hour basis while active. You can connect directly via SSH access and deploy pre-configured templates including PyTorch/CUDA, vLLM, Ollama, and ComfyUI.

GPU Cloud · NVIDIA · Ampere

Rent NVIDIA RTX A2000 GPU by the hour

The NVIDIA RTX A2000 is a compact Ampere-generation workstation GPU designed for entry-level compute and graphics workloads on Cloudj. It pairs 6 GB of VRAM and a 70 W power profile with about 11.2 TFLOPS of single-precision performance and about 247 GB/s of memory bandwidth. With CUDA compute capability 8.6, this card supports modern AI frameworks for lightweight development and testing.

Rent RTX A2000 All GPUs

Specifications

VRAM per GPU

6 GB

Architecture

NVIDIA Ampere

Compute capability

8.6

FP32 compute

≈ 11.2 TFLOPS

Memory bandwidth

≈ 247 GB/s

Power limit

70 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 RTX A2000 is good for

Code Prototyping and Debugging

Develop and test CUDA and PyTorch scripts on an Ampere architecture with compute capability 8.6 before scaling up to larger hardware.

Compact Language Model Inference

Run small, heavily quantized language models using Ollama or vLLM within the 6 GB VRAM limit.

Basic Image Generation

Execute lightweight diffusion pipelines in ComfyUI that are optimized to fit inside a 6 GB memory footprint.

Workstation Rendering and CAD

Handle light rendering tasks and graphics workflows leveraging about 247 GB/s of memory bandwidth.

Is it the right GPU for you?

Choose the RTX A2000 for low-overhead code debugging, environment setup, and running compact models that fit into 6 GB of VRAM. If your workload involves training, fine-tuning, or running larger unquantized models, select a workstation or datacenter GPU with higher memory capacity and compute throughput.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA RTX A2000	6 GB	≈ 11.2 TFLOPS	70 W
NVIDIA Q RTX 4000	8 GB	≈ 7.7 TFLOPS	125 W	Details →
NVIDIA RTX A4000	16 GB	≈ 20.6 TFLOPS	140 W	Details →
NVIDIA RTX 4500Ada	24 GB	≈ 38.2 TFLOPS	210 W	Details →
NVIDIA RTX PRO 4000	24 GB	≈ 44.3 TFLOPS	145 W	Details →

Frequently asked questions

Rent the RTX A2000 by the hour

Create an account, choose a configuration and connect over SSH. Billing stops when you delete the GPU.

Rent RTX A2000 All GPUs