Will modern frameworks run on the GTX TITAN X?

Often not out of the box. Compute capability 5.2 is no longer targeted by many current CUDA and PyTorch builds. Check the CUDA and framework versions in your template or Docker image before you deploy.

What is the GTX TITAN X good for today?

Legacy code, FP32 compute and memory-fit tests on 12 GB. For new AI projects, an RTX 3060 (12 GB, Ampere, 12 TFLOPS) runs current software with Tensor Cores and BF16.

GTX TITAN X vs Titan Xp?

Both have 12 GB. The Pascal Titan Xp is listed with about 11.7 TFLOPS and 410 GB/s, against 6.8 TFLOPS and 239 GB/s on the Maxwell TITAN X.

How do I limit cost on Cloudj?

Rent by the hour, stop the server between sessions so only the disk is billed, and delete it when you are done.

GPU Cloud · NVIDIA · Maxwell

Rent an NVIDIA GTX TITAN X GPU with 12 GB VRAM

Rent GTX TITAN X cloud GPU servers by the hour on Cloudj: 12 GB VRAM on NVIDIA Maxwell with about 6.8 TFLOPS FP32 and 239 GB/s of bandwidth. It is the oldest card in our list, useful for legacy CUDA code, FP32 experiments and 12 GB memory tests where modern features are not required (150 W, compute capability 5.2). Check that your CUDA and framework versions still support this GPU, pick a live configuration and connect over SSH with your key.

Rent GTX TITAN X All GPUs

GTX TITAN X 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.

Live GTX TITAN X configurations and prices

Out of stock right now

Browse available GPUs

What you can run on the GTX TITAN X cloud GPU

Legacy CUDA and research code

Run older codebases that target Maxwell or early CUDA toolchains without porting them first.

12 GB memory experiments

Test whether a dataset or model fits in 12 GB before moving to a faster card.

Quantized 7B models

Run 7B GGUF models at 4-bit in llama.cpp-based tools where the build supports Maxwell.

FP32 compute jobs

Batch simulations and numerical scripts that only need plain FP32.

Is the GTX TITAN X the right GPU for your workload?

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.

GTX TITAN X vs similar cloud 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 →

Guides for GTX TITAN X

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.

All articles →

GTX TITAN X cloud GPU: frequently asked questions

Rent the GTX TITAN X 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 TITAN X All GPUs