SurferCloud GPU Cloud Servers: Cost-Effective RTX 4090 & Tesla P40 Instances for AI, Rendering, and HPC

As AI workloads continue to grow and computing demands become increasingly GPU-driven, SurferCloud delivers a practical solution for developers, researchers, studios, and enterprises seeking high-performance GPU computing at a competitive price.

Get Started: https://console.surfercloud.com/uhost/uhost/gpu_create

SurferCloud now offers two major GPU product families:

• RTX 4090 GPU Cloud Servers—Hong Kong Data Center
• Tesla P40 GPU Cloud Servers—Singapore Data Center

Both product lines support Linux & Windows, come with pre-installed NVIDIA drivers, and provide a wide range of CPU, memory, and GPU configurations.
This makes them suitable for AI training, AI inference, 3D rendering, video generation, simulation computing, and more.

Why Choose SurferCloud GPU Servers?

✔ Real RTX 4090 / Tesla P40 GPUs, Not Virtualized

Each GPU instance provides dedicated GPU resources. No noisy neighbors, no shared GPU cores.

✔ Flexible CPU & Memory Options

From 4 cores up to 188 cores, and from 8 GB RAM to 940 GB RAM—ideal for everything from entry-level AI tasks to large-scale HPC workloads.

✔ Optimized Network & Asian Data Centers

Hong Kong and Singapore locations ensure excellent connectivity to China and global regions.

✔ Ready-to-use Images with NVIDIA Drivers

All images include:

• Pre-installed NVIDIA drivers
• CUDA toolkit
• Ubuntu 22.04 64bit
• Optional Windows support

This allows you to start training models immediately without any driver compatibility issues.

Hong Kong RTX 4090 GPU Cloud Servers

Powered by NVIDIA RTX 4090, delivering up to 83 TFLOPS (FP32) per GPU.
All configurations include 24 GB VRAM per GPU.

Available Plans (Hong Kong – RTX 4090)

Get Started: https://console.surfercloud.com/uhost/uhost/gpu_create

CPU Model	GPU Count	CPU Cores	Memory	VRAM	Theoretical Performance
AMD x86_64	1 × RTX4090	16 cores	32 GB	24 GB	83 TFLOPS
AMD x86_64	1 × RTX4090	16 cores	64 GB	24 GB	83 TFLOPS
AMD x86_64	2 × RTX4090	32 cores	64 GB	48 GB	166 TFLOPS
AMD x86_64	2 × RTX4090	32 cores	128 GB	48 GB	166 TFLOPS
AMD x86_64	4 × RTX4090	64 cores	128 GB	96 GB	332 TFLOPS
AMD x86_64	4 × RTX4090	64 cores	256 GB	96 GB	332 TFLOPS
AMD x86_64	4 × RTX4090	92 cores	470 GB	96 GB	332 TFLOPS
AMD x86_64	8 × RTX4090	92 cores	440 GB	192 GB	664 TFLOPS
AMD x86_64	8 × RTX4090	124 cores	440 GB	192 GB	664 TFLOPS
AMD x86_64	8 × RTX4090	124 cores	512 GB	192 GB	664 TFLOPS
AMD x86_64	8 × RTX4090	124 cores	680 GB	192 GB	664 TFLOPS
AMD x86_64	8 × RTX4090	124 cores	940 GB	192 GB	664 TFLOPS
AMD x86_64	8 × RTX4090	188 cores	940 GB	192 GB	664 TFLOPS
AMD x86_64	10 × RTX4090	140 cores	440 GB	240 GB	830 TFLOPS

Intel variants are also available, mirroring the same configurations.

Pre-Installed Software

• NVIDIA Driver: nvidia 570.133.07
• CUDA Version: CUDA 12.8
• OS: Ubuntu 22.04 64bit (Windows optional)

Singapore Tesla P40 GPU Cloud Servers

For users looking for a cost-efficient solution for inference or lightweight training, the Tesla P40 remains a strong performer with 12 TFLOPS per GPU and 24 GB VRAM.

Available Plans (Singapore – Tesla P40)

Get Started: https://console.surfercloud.com/uhost/uhost/gpu_create

CPU Model	GPU Count	CPU Cores	Memory	VRAM	Theoretical Performance
Intel x86_64	1 × P40	4 cores	8 GB	24 GB	12 TFLOPS
Intel x86_64	1 × P40	4 cores	16 GB	24 GB	12 TFLOPS
Intel x86_64	1 × P40	8 cores	16 GB	24 GB	12 TFLOPS
Intel x86_64	1 × P40	8 cores	32 GB	24 GB	12 TFLOPS
Intel x86_64	1 × P40	8 cores	64 GB	24 GB	12 TFLOPS
Intel x86_64	2 × P40	8 cores	16 GB	48 GB	24 TFLOPS
Intel x86_64	2 × P40	8 cores	32 GB	48 GB	24 TFLOPS
Intel x86_64	2 × P40	16 cores	32 GB	48 GB	24 TFLOPS
Intel x86_64	2 × P40	16 cores	64 GB	48 GB	24 TFLOPS
Intel x86_64	4 × P40	16 cores	32 GB	96 GB	48 TFLOPS
Intel x86_64	4 × P40	16 cores	64 GB	96 GB	48 TFLOPS
Intel x86_64	4 × P40	32 cores	64 GB	96 GB	48 TFLOPS
Intel x86_64	4 × P40	32 cores	128 GB	96 GB	48 TFLOPS
Intel x86_64	8 × P40	44 cores	440 GB	192 GB	96 TFLOPS

Pre-Installed Software

• NVIDIA Driver: nvidia 550.127.08
• CUDA Toolkit: CUDA 12.x compatible
• OS: Ubuntu 22.04 64-bit (Windows optional)

Use Cases

AI Training & Fine-tuning

• Large language models
• Diffusion-based AI image/video generation
• Speech & NLP models

AI Inference Workloads

• Chatbots
• Recommender systems
• CV-based production inference pipelines

Rendering & Simulation

• 3D rendering (Blender, Octane, Redshift)
• VFX pipelines
• GPU-accelerated simulations

Scientific & Engineering Computing

• CUDA-based scientific computation
• HPC simulations
• Research workloads

FAQ

1. Do these servers support both Linux and Windows?

Yes. All plans support Ubuntu 22.04 and optional Windows images.

2. Are NVIDIA drivers pre-installed?

Yes. Instances ship with validated NVIDIA drivers and CUDA, ready for immediate use.

3. Are GPU resources dedicated?

Yes. Every instance includes dedicated RTX 4090 or Tesla P40 GPUs (not shared, not vGPU).

4. Who are these GPU servers suitable for?

Developers, AI researchers, AIGC creators, rendering studios, enterprises, and educational institutions.

5. Can I scale up to multi-GPU clusters?

Yes. Up to 10× RTX 4090 or 8× Tesla P40 are available.

Conclusion

SurferCloud’s GPU cloud offerings strike an excellent balance between price, performance, and scalability.
Whether you're conducting high-end AI training on RTX 4090 clusters or running cost-effective inference workloads on Tesla P40, SurferCloud provides reliable, optimized infrastructure tailored for modern GPU computing.