Is the RTX A4000 suitable for LLM inference and fine-tuning?

With 16 GB of VRAM and about 372 GB/s of memory bandwidth, it handles inference for smaller or quantized language models via Ollama or vLLM, as well as lightweight fine-tuning. For full-parameter fine-tuning or running larger unquantized models, a GPU with more VRAM is recommended.

How does this GPU compare to larger or smaller options?

As a 16 GB workstation card offering about 20.6 TFLOPS of single-precision compute, the RTX A4000 sits between entry-level consumer cards and heavier workstation or datacenter GPUs. Stepping down reduces available memory for batch sizes, while stepping up provides greater VRAM capacity and memory bandwidth for heavier workloads.

What software environments are available for the RTX A4000 on Cloudj?

You can deploy ready templates including PyTorch/CUDA, vLLM, Ollama, and ComfyUI, all compatible with the card's compute capability 8.6. Full SSH access is also provided so you can customize your environment.

How does billing work when renting the RTX A4000?

Rentals on Cloudj use per-hour billing, allowing you to pay only for the time your instance is active.

GPU Cloud · NVIDIA · Ampere

Rent NVIDIA RTX A4000 GPU by the hour

The NVIDIA RTX A4000 is an Ampere-architecture workstation GPU equipped with 16 GB of VRAM and compute capability 8.6. Operating within a 140 W power envelope, it delivers about 20.6 TFLOPS of single-precision compute and about 372 GB/s of memory bandwidth. Rent it on Cloudj with per-hour billing, full SSH access, and pre-configured templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI.

Rent RTX A4000 All GPUs

Specifications

VRAM per GPU

16 GB

Architecture

NVIDIA Ampere

Compute capability

8.6

FP32 compute

≈ 20.6 TFLOPS

Memory bandwidth

≈ 372 GB/s

Power limit

140 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 A4000 is good for

Moderate Language Model Inference

Run quantized or medium-sized language models using Ollama or vLLM within the 16 GB VRAM capacity.

Image Generation Workflows

Deploy ComfyUI templates for diffusion-based image generation and experimentation backed by about 372 GB/s of memory bandwidth.

Deep Learning Prototyping

Develop and test PyTorch/CUDA training scripts on an Ampere workstation architecture with compute capability 8.6.

Workstation Rendering Tasks

Execute rendering and simulation workloads that benefit from about 20.6 TFLOPS of single-precision performance.

Is it the right GPU for you?

The RTX A4000 suits developers and creators who need a reliable 16 GB Ampere workstation GPU for inference, image generation, and code prototyping. Choose a smaller GPU for basic testing with lower memory needs, or step up to a larger datacenter or workstation card if your models exceed 16 GB of VRAM or require higher memory bandwidth.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA RTX A4000	16 GB	≈ 20.6 TFLOPS	140 W
NVIDIA Q RTX 4000	8 GB	≈ 7.7 TFLOPS	125 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 →
NVIDIA RTX A2000	6 GB	≈ 11.2 TFLOPS	70 W	Details →

Frequently asked questions

Rent the RTX A4000 by the hour

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

Rent RTX A4000 All GPUs