Stable Diffusion Hosting | Private AI Image Generation on GPU – B2BHostingClub

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Choose The Best GPUs for Stable Diffusion Service Hosting

Express GPU Dedicated Server - P1000

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  • 32GB RAM
  • GPU: Nvidia Quadro P1000
  • Eight-Core Xeon E5-2690
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Pascal
  • CUDA Cores: 640
  • GPU Memory: 4GB GDDR5
  • FP32 Performance: 1.894 TFLOPS

Basic GPU Dedicated Server - T1000

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  • 64GB RAM
  • GPU: Nvidia Quadro T1000
  • Eight-Core Xeon E5-2690
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Turing
  • CUDA Cores: 896
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 2.5 TFLOPS

Basic GPU Dedicated Server - GTX 1650

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  • 64GB RAM
  • GPU: Nvidia GeForce GTX 1650
  • Eight-Core Xeon E5-2667v3
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Turing
  • CUDA Cores: 896
  • GPU Memory: 4GB GDDR5
  • FP32 Performance: 3.0 TFLOPS

Basic GPU Dedicated Server - GTX 1660

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  • 64GB RAM
  • GPU: Nvidia GeForce GTX 1660
  • Dual 8-Core Xeon E5-2660
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Turing
  • CUDA Cores: 1408
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 5.0 TFLOPS

Advanced GPU Dedicated Server - V100

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  • 128GB RAM
  • GPU: Nvidia V100
  • Dual 12-Core E5-2690v3
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Volta
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS

Professional GPU Dedicated Server - RTX 2060

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  • 128GB RAM
  • GPU: Nvidia GeForce RTX 2060
  • Dual 8-Core E5-2660
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ampere
  • CUDA Cores: 1920
  • Tensor Cores: 240
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 6.5 TFLOPS

Advanced GPU Dedicated Server - RTX 2060

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  • 128GB RAM
  • GPU: Nvidia GeForce RTX 2060
  • Dual 20-Core Gold 6148
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ampere
  • CUDA Cores: 1920
  • Tensor Cores: 240
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 6.5 TFLOPS

Advanced GPU Dedicated Server - RTX 3060 Ti

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  • 128GB RAM
  • GPU: GeForce RTX 3060 Ti
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ampere
  • CUDA Cores: 4864
  • Tensor Cores: 152
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 16.2 TFLOPS

Professional GPU VPS - A4000

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  • 32GB RAM
  • Dedicated GPU: Quadro RTX A4000
  • 24 CPU Cores
  • 320GB SSD
  • 300Mbps Unmetered Bandwidth
  • OS: Linux / Windows 10/11
  • Once per 2 Weeks Backup
  • Single GPU Specifications:
  • CUDA Cores: 6,144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPS

Advanced GPU Dedicated Server - A4000

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  • 12GB RAM
  • GPU: Nvidia Quadro RTX A4000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ampere
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPS

Advanced GPU Dedicated Server - A5000

/mo

  • 128GB RAM
  • GPU: Nvidia Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ampere
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS

Enterprise GPU Dedicated Server - A40

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  • 256GB RAM
  • GPU: Nvidia A40
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 37.48 TFLOPS

Basic GPU Dedicated Server - RTX 5060

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  • 64GB RAM
  • GPU: Nvidia GeForce RTX 5060
  • 24-Core Platinum 8160
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Blackwell 2.0
  • CUDA Cores: 4608
  • Tensor Cores: 144
  • GPU Memory: 8GB GDDR7
  • FP32 Performance: 23.22 TFLOPS
  • This is a pre-sale product. Delivery will be completed within 2–7 days after payment.

Enterprise GPU Dedicated Server - RTX 5090

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  • 256GB RAM
  • GPU: GeForce RTX 5090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Blackwell 2.0
  • CUDA Cores: 21,760
  • Tensor Cores: 680
  • GPU Memory: 32 GB GDDR7
  • FP32 Performance: 109.7 TFLOPS
  • This is a pre-sale product. Delivery will be completed within 2–10 days after payment.

Enterprise GPU Dedicated Server - A100

/mo

  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS

Enterprise GPU Dedicated Server - A100(80GB)

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  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 19.5 TFLOPS

Enterprise GPU Dedicated Server - H100

/mo

  • 256GB RAM
  • GPU: Nvidia H100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Hopper
  • CUDA Cores: 14,592
  • Tensor Cores: 456
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 183TFLOPS

Multi-GPU Dedicated Server- 2xRTX 4090

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  • 256GB RAM
  • GPU: 2 x GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ada Lovelace
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS

Multi-GPU Dedicated Server- 2xRTX 5090

/mo

  • 256GB RAM
  • GPU: 2 x GeForce RTX 5090
  • Dual E5-2699v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Blackwell 2.0
  • CUDA Cores: 21,760
  • Tensor Cores: 680
  • GPU Memory: 32 GB GDDR7
  • FP32 Performance: 109.7 TFLOPS
  • This is a pre-sale product. Delivery will be completed within 2–10 days after payment.

Multi-GPU Dedicated Server - 3xV100

/mo

  • 256GB RAM
  • GPU: 3 x Nvidia V100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Volta
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS

Multi-GPU Dedicated Server - 3xRTX A5000

/mo

  • 256GB RAM
  • GPU: 3 x Quadro RTX A5000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS

Multi-GPU Dedicated Server - 3xRTX A6000

/mo

  • 256GB RAM
  • GPU: 3 x Quadro RTX A6000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS

Multi-GPU Dedicated Server - 4xA100

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  • 512GB RAM
  • GPU: 4 x Nvidia A100
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS

Multi-GPU Dedicated Server - 4xRTX A6000

/mo

  • 512GB RAM
  • GPU: 4 x Quadro RTX A6000
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS

Basic GPU Dedicated Server - RTX 4060

/mo

  • 64GB RAM
  • GPU: Nvidia GeForce RTX 4060
  • Eight-Core E5-2690
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 3072
  • Tensor Cores: 96
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 15.11 TFLOPS

Stable Diffusion Model Hosting Compatibility Matrix

This table provides a detailed overview of the most widely used Stable Diffusion models, evaluating their compatibility with GPU types, web interfaces (like ComfyUI or AUTOMATIC1111), and advanced features such as LoRA, ControlNet, and SDXL Refiner support. It also highlights whether additional components like FFmpeg are needed for audio/video models, and clarifies each model's licensing terms—critical for commercial or research deployments.

Model
Name
Size
(fp16)
Recommended
GPU
Figure/sec
LoRA
Support
ControlNet
Support
Recommended
UI
Suit for
Refiner?
Additional
components
required
License
Agreement
stabilityai/stable-diffusion-v1-4 ~4.27GB RTX3060/5060 1.5-2 ✅(needs expansion) AUTOMATIC1111 none CreativeML OpenRAIL-M
stabilityai/stable-diffusion-v1-5 ~4.27GB RTX3060/5060 1.8-2.2 AUTOMATIC1111 none CreativeML OpenRAIL-M
stabilityai/stable-diffusion-xl-base-1.0 ~6.76GB A4000/A5000 1.2-1.5 ✅ (SDXL version required) ComfyUI none CreativeML OpenRAIL++-M
stabilityai/stable-diffusion-xl-refiner-1.0 ~6.74GB A4000/A5000 0.8-1.1 ComfyUI ✅(As a Refiner) none CreativeML OpenRAIL++-M
stabilityai/stable-audio-open-1.0 ~7.6GB A4000/A5000 -- Web UI FFmpeg, TTS preprocessing Non-commercial RAIL
stabilityai/stable-video-diffusion-img2vid-xt ~8GB A4000/A5000 Depends on the frame rate Web UI FFmpeg Non-commercial RAIL
stabilityai/stable-diffusion-2 ~5.2GB RTX 3060 / 5060 1.6-2.0 AUTOMATIC1111 none CreativeML OpenRAIL-M
stabilityai/stable-diffusion-3-medium ~10GB RTX4090 / 5090 1.0-1.5 Partial support ComfyUI none Not open source, requires API license
stabilityai/stable-diffusion-3.5-large ~20GB A100-40GB / RTX5090 0.5-0.9 unknown unknown Web UI / API ✅ (Need to combine with Refiner) unknown API-only license
stabilityai/stable-diffusion-3.5-large-turbo ~20GB A100-40GB / RTX5090 >2.0 unknown unknown Web UI / API ✅ (Need to combine with Refiner) unknown API-only license

Features of Stable Diffusion Service

Full SD Models

Run any version of Stable Diffusion—SD 1.5, 2.1, SDXL, or SD 3.5—on your terms. Choose your UI (ComfyUI or AUTOMATIC1111), customize pipelines, switch checkpoints, and fine-tune models with LoRA or ControlNet integration.

High Performance & Scalability

Deploy on powerful GPUs (e.g. RTX 4090, A100) for fast, multi-user inference. Handle image, audio, or even video generation at scale, with support for batching, concurrency, and memory-efficient backends like vLLM.

Data Privacy & Offline Capability

Self-hosted means no third-party API calls. Keep your prompts, generations, and models completely private—ideal for secure environments or enterprise use cases. Run everything fully offline once models are downloaded.

Modular UI Support (ComfyUI / A1111)

Use AUTOMATIC1111 for quick generation and ease of use, or ComfyUI for advanced, node-based workflows supporting Refiner stages, multi-model chaining, and fine-grained control—all with visual, drag-and-drop interfaces.

Frequently asked questions

AUTOMATIC1111 offers a feature-rich, web-based interface ideal for quick image generation, prompt crafting, and model switching. ComfyUI is a node-based workflow engine, better suited for advanced pipelines, fine-grained control, multi-model setups, and automation.
SD 1.5: 8–12GB VRAM (e.g., RTX 3060 / A4000)
SDXL Base/Refiner: 24–32GB+ (e.g., RTX4090, RTX5090)
SD 3.5 / Video/Audio Models: 40–80GB+ (e.g., A100, H100)
Yes, especially with a high-memory GPU and optimized backend like vLLM or TorchServe. For production, containerization and GPU scheduling are recommended.
Not necessarily. Once models and weights are downloaded, both UIs can run fully offline, ideal for secure or air-gapped environments.
If you’re a beginner or want fast experimentation, start with AUTOMATIC1111. If you need precision control, complex pipelines, or SDXL/Refiner integration, ComfyUI is recommended.
AUTOMATIC1111 supports both natively. ComfyUI supports them via custom nodes, often offering deeper customization and flexibility.
SD 1.5 / SDXL are available under CreativeML OpenRAIL-M licenses. SD 3.x / audio/video models may have non-commercial or API-only licenses—always review usage terms before deployment.
Yes. Both ComfyUI and AUTOMATIC1111 have API support or can be wrapped via Python, FastAPI, or Flask for full automation.

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