Is the GTX TITAN X suitable for LLM inference and image generation?

With 12 GB of VRAM, it can hold small models for inference in Ollama or basic pipelines in ComfyUI. However, because it uses the older Maxwell architecture with compute capability 5.2 and about 6.8 TFLOPS of single-precision performance, generation will be slower than on newer GPUs.

Does this GPU support modern frameworks like PyTorch and vLLM?

Cloudj provides ready templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI. Because the GTX TITAN X is a Maxwell-generation card with compute capability 5.2, some newer software features or quantization methods in tools like vLLM may require a GPU with a newer architecture.

How does the GTX TITAN X compare to smaller or larger GPUs?

Its 12 GB of VRAM provides more room for data and weights than smaller entry-level consumer cards. Compared to newer workstation or datacenter GPUs, its about 239 GB/s of memory bandwidth and Maxwell architecture make it better suited for light tasks rather than heavy production workloads.

How do billing and access work on Cloudj?

Rentals are billed on a per-hour basis while your instance is active. You can connect directly to your environment via SSH access or launch pre-configured templates straight from the dashboard.

GPU Cloud · NVIDIA · Maxwell

Rent NVIDIA GTX TITAN X GPU by the hour

The NVIDIA GTX TITAN X is a Maxwell-architecture consumer GPU available for rental on Cloudj. It offers 12 GB of VRAM, about 6.8 TFLOPS of single-precision compute, and about 239 GB/s of memory bandwidth within a 150 W power envelope. Supporting CUDA compute capability 5.2, it handles legacy GPU tasks, development, and entry-level deep learning workflows.

Rent GTX TITAN X All GPUs

Specifications

VRAM per GPU

12 GB

Architecture

NVIDIA Maxwell

Compute capability

5.2

FP32 compute

≈ 6.8 TFLOPS

Memory bandwidth

≈ 239 GB/s

Power limit

150 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 GTX TITAN X is good for

Legacy CUDA Development

Test and maintain software targeting the Maxwell architecture and CUDA compute capability 5.2.

Entry-Level Deep Learning

Run basic training and prototyping workflows in PyTorch that fit within 12 GB of VRAM.

Lightweight Model Inference

Deploy small language or vision models using templates like Ollama where moderate memory bandwidth of about 239 GB/s is sufficient.

Standard Image Generation

Execute basic diffusion pipelines in ComfyUI that require up to 12 GB of video memory without demanding modern tensor cores.

Is it the right GPU for you?

Choose the GTX TITAN X when you need 12 GB of VRAM for development, legacy software compatibility, or light inference without paying for newer architectures. Select a smaller GPU if your workload uses minimal memory, or upgrade to a newer, larger GPU if you need modern compute capabilities and higher memory bandwidth for large-scale training.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA GTX TITAN X	12 GB	≈ 6.8 TFLOPS	150 W
NVIDIA Titan Xp	12 GB	≈ 11.7 TFLOPS	160 W	Details →
NVIDIA GTX 1080 Ti	11 GB	≈ 11 TFLOPS	250 W	Details →
NVIDIA RTX 2080 Ti	11 GB	≈ 14.6 TFLOPS	250 W	Details →
NVIDIA GTX 1070 Ti	8 GB	≈ 7.4 TFLOPS	180 W	Details →

Frequently asked questions

Rent the GTX TITAN X by the hour

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

Rent GTX TITAN X All GPUs