Does the GTX 1660 S have Tensor Cores?

No. GTX 16-series Turing cards have no Tensor Cores, so FP16 mixed precision gives little speedup. Use an RTX-series card for training.

Can the GTX 1660 S run Stable Diffusion?

SD 1.5 can run with low-VRAM settings, slowly. For SDXL or Flux, a 12 GB card like the RTX 3060 is a much better choice.

GTX 1660 S vs GTX 1660 Ti?

Both have 6 GB on Turing. The GTX 1660 Ti is listed with more FP32 (5.3 vs 3.6 TFLOPS), while the 1660 S has more bandwidth (277 vs 234 GB/s).

How does billing work on Cloudj?

Per minute from your account balance while it runs, at the price locked when you deploy, plus bandwidth per GB. A stopped instance is charged for storage only. There are no monthly GPU plans.

GPU Cloud · NVIDIA · Turing

Rent an NVIDIA GTX 1660 S GPU with 6 GB VRAM

Rent GTX 1660 S cloud GPU servers by the hour on Cloudj: 6 GB VRAM on NVIDIA Turing with about 3.6 TFLOPS FP32 and 277 GB/s of memory bandwidth. It is a budget-tier card for learners, light inference and GPU-accelerated scripts that fit in 6 GB, at a 125 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.

Rent GTX 1660 S All GPUs

GTX 1660 S specifications

VRAM per GPU

6 GB

Architecture

NVIDIA Turing

Compute capability

7.5

FP32 compute

≈ 3.6 TFLOPS

Memory bandwidth

≈ 277 GB/s

Power limit

125 W

Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.

Live GTX 1660 S configurations and prices

Out of stock right now

Browse available GPUs

What you can run on the GTX 1660 S cloud GPU

Learning GPU workflows

Practice CUDA setup, Python environments and model loading on a dedicated Turing GPU.

Small LLMs at 4-bit

Run 3B-class models, or tight 7B models at 4-bit, through Ollama for quick tests.

Lightweight vision inference

Serve detection, segmentation or OCR models that need a GPU but not much memory.

Pipeline smoke tests

Check that preprocessing, inference and output steps work on a GPU before scaling up.

Is the GTX 1660 S the right GPU for your workload?

Choose the GTX 1660 S for budget-conscious development, code testing, and lightweight inference tasks that fit within 6 GB of VRAM. Opt for a GPU with more memory and higher compute throughput if you need to fine-tune modern foundation models or run large unquantized workloads, as this is already a compact entry-level option.

GTX 1660 S vs similar cloud GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA GTX 1660 S	6 GB	≈ 3.6 TFLOPS	125 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 1080 Ti	11 GB	≈ 11 TFLOPS	250 W	Details →
NVIDIA GTX 1650	4 GB	≈ undefined TFLOPS	W	Details →

Guides for GTX 1660 S

GPU cloud Asia guide: Japan, Singapore, Korea and nearbyA practical GPU cloud Asia guide: which country codes to search for East and Southeast Asian users, a fallback order, and how to pair a GPU with a VPS.GPU cloud by location: how to pick a country for your GPUHow GPU cloud by location works on Cloudj: filter a live list of machines by country code, decide between near users, lowest price or data rules.GPU cloud Europe: choosing a country for your AI workloadsA practical guide to GPU cloud Europe searches: filter by several country codes at once, weigh Western, Nordic and Eastern hosts, and plan where data lives.GPU cloud North America: renting GPUs in the US and CanadaA GPU cloud North America guide: how to search US and CA hosts, read the results, pick a VPS front end and decide when a US GPU suits users elsewhere.

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GTX 1660 S cloud GPU: frequently asked questions

Rent the GTX 1660 S 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.

Rent GTX 1660 S All GPUs