Is the RTX 2060S suitable for LLM inference and fine-tuning?

With 8 GB of VRAM and about 370 GB/s of memory bandwidth, the RTX 2060S can handle inference for small, quantized language models via Ollama or vLLM. Fine-tuning or running larger models requires a GPU with a higher memory capacity.

How does this Turing GPU handle image generation?

The RTX 2060S supports standard image generation workflows in ComfyUI as long as the model checkpoints and intermediate tensors stay within the 8 GB VRAM limit. Users working with larger diffusion models or high-resolution batches should select a card with more memory.

How does the RTX 2060S compare to larger GPUs?

As a consumer Turing card with compute capability 7.5 and about 7.5 TFLOPS of single-precision throughput, it is designed for lighter workloads compared to newer architectures or workstation and datacenter GPUs that offer more VRAM and higher memory bandwidth.

How do billing and instance access work on Cloudj?

Instances are billed on a per-hour basis while active. You can connect directly via SSH or start immediately with ready templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI.

GPU Cloud · NVIDIA · Turing

Rent NVIDIA RTX 2060S GPU by the hour

The NVIDIA RTX 2060S is a consumer-segment GPU built on the Turing architecture with 8 GB of VRAM and compute capability 7.5. On Cloudj, it delivers about 7.5 TFLOPS of single-precision performance and about 370 GB/s of memory bandwidth inside a 175 W power envelope. You can launch instances with per-hour billing, full SSH access, and preconfigured templates including PyTorch/CUDA, vLLM, Ollama, and ComfyUI.

Rent RTX 2060S All GPUs

Specifications

VRAM per GPU

8 GB

Architecture

NVIDIA Turing

Compute capability

7.5

FP32 compute

≈ 7.5 TFLOPS

Memory bandwidth

≈ 370 GB/s

Power limit

175 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 2060S is good for

Compact Language Model Inference

Run small quantized models using Ollama or vLLM within the 8 GB VRAM capacity.

Image Generation Workflows

Execute standard diffusion pipelines in ComfyUI backed by about 370 GB/s of memory bandwidth.

Code Prototyping and Debugging

Develop and test PyTorch/CUDA scripts on a Turing GPU with compute capability 7.5 before scaling up.

Lightweight Model Training

Train small neural networks and tabular models using about 7.5 TFLOPS of single-precision compute.

Is it the right GPU for you?

Choose the RTX 2060S for entry-level development, testing, and lightweight inference tasks that fit comfortably into 8 GB of memory. Opt for a GPU with more VRAM and higher compute if you need to fine-tune large models or run heavy production workloads, or a smaller GPU only for basic display or minimal compute tasks.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA RTX 2060S	8 GB	≈ 7.5 TFLOPS	175 W
NVIDIA GTX 1070 Ti	8 GB	≈ 7.4 TFLOPS	180 W	Details →
NVIDIA GTX 1080	8 GB	≈ 7.8 TFLOPS	130 W	Details →
NVIDIA GTX 1660 Ti	6 GB	≈ 5.3 TFLOPS	120 W	Details →
NVIDIA GTX 1080 Ti	11 GB	≈ 11 TFLOPS	250 W	Details →

Frequently asked questions

Rent the RTX 2060S by the hour

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

Rent RTX 2060S All GPUs