Flux.1 Hosting | Next-Gen AI Image Generation on GPU – B2BHostingClub

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Choose The Best GPUs for Flux.1 Hosting Service

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

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  • 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 - RTX 4090

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  • 256GB RAM
  • GPU: GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • 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 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

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.

Multi-GPU Dedicated Server- 2xRTX 5090

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  • 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.

Enterprise GPU Dedicated Server - A100

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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: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS

Enterprise GPU Dedicated Server - H100

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  • 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

Enterprise GPU Dedicated Server - RTX A6000

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  • 256GB RAM
  • GPU: Nvidia Quadro RTX A6000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS

Multi-GPU Dedicated Server - 4xRTX A6000

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  • 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

Flux Service Hosting Compatibility Matrix

Compatibility of FLUX model versions (e.g. dev, schnell, etc.) across different deployment frameworks, inference tools, and application platforms.

Model Name
License
Parameters
Inference Frameworks
Web UIs Support
Min GPU VRAM
Notes
black-forest-labs/FLUX.1-dev Non-Commercial ~12B diffusers, transformers, vLLM, torch.compile ❌ AUTOMATIC1111✅ ComfyUI (via node) ≥24 GB Dev version; slower inference, higher quality
black-forest-labs/FLUX.1-schnell Apache 2.0 ~12B diffusers, transformers, vLLM, torch.compile ✅ ComfyUI✅ custom UIs ≥16 GB Speed-optimized, lower memory cost

Features of Flux Hosting Service

Self-Hosted Creative Control

Run FLUX models (like flux.1-schnell) on your own GPU server or cloud instance. You have full control over model versions, configurations, and customizations—ideal for researchers and creators.

Optimized for High-Quality Stylized Image Generation

FLUX.1 is designed for generating artistic and stylized outputs. Hosting it enables fast inference with minimal latency, especially when paired with optimized frameworks like Hugging Face diffusers.

Integrates with ComfyUI or Custom Pipelines

Supports modern UI-based workflows like ComfyUI, or can be run via code-based APIs (Python scripts, REST APIs). Perfect for building internal tools or automated image generation platforms.

LoRA & Extension Compatibility

Compatible with LoRA fine-tuning and control modules like ControlNet (depending on model architecture). Enables targeted customization for different artistic needs or datasets.

Frequently asked questions

FLUX.1 is a high-quality, stylized text-to-image generation model developed by Black Forest Labs. Versions like FLUX.1-dev and FLUX.1-schnell are publicly available on Hugging Face for research and creative use.
You’ll need:
A GPU with at least 16–24 GB VRAM (e.g., A100, 4090, 3090, H100)
Linux server (Ubuntu 20.04+)
CUDA-compatible environment
Python 3.10+ with PyTorch + Diffusers stack
Yes, you can fine-tune FLUX.1 models with LoRA or DreamBooth, assuming architecture support and sufficient VRAM. Tools like PEFT, Diffusers, and Kohya GUI can be used with proper configuration.
Yes, especially FLUX.1-schnell, which is optimized for fast inference. It’s ideal for stylized generation platforms, internal creative tools, and on-demand art services.
FLUX.1-dev: Early version focused on experimental outputs and complex visual styles. FLUX.1-schnell: A faster and more optimized variant, ideal for low-latency deployment and production environments.
Use Hugging Face diffusers for official compatibility. Optionally, integrate with:
ComfyUI (node-based visual UI)
AUTOMATIC1111 (requires conversion to .ckpt)
Custom Python API or Web UI
Yes. You can load the model as a checkpoint in ComfyUI, or manually integrate it using a custom node or VAE/UNet loading script. Make sure dependencies like safetensors, xformers, and torch are properly installed.

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