vLLM Hosting | High-Performance GPU LLM Deployment – B2BHostingClub

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Choose Your GPU Server for vLLM Hosting Plans

B2BHOSTINGCLUB offers best budget GPU servers for vLLM. Cost-effective vLLM hosting is ideal to deploy your own AI Chatbot. Note that the total size of the GPU memory should not be less than 1.2 times the model size.

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 - 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 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

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

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

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

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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

vLLM vs Ollama vs SGLang vs TGI vs Llama.cpp

vLLM is best suited for applications that demand efficient, real-time processing of large language models.

DeepSeek

Features
vLLM
Ollama
SGLang
TGI(HF)
Llama.cpp
Optimized for GPU (CUDA) CPU/GPU/M1/M2 GPU/TPU GPU (CUDA) CPU/ARM
Performance High Medium High Medium Low
Multi-GPU ✅ Yes ✅ Yes ✅ Yes ✅ Yes ❌ No
Streaming ✅ Yes ✅ Yes ✅ Yes ✅ Yes ✅ Yes
API Server ✅ Yes ✅ Yes ✅ Yes ✅ Yes ❌ No
Memory Efficient ✅ Yes ✅ Yes ✅ Yes ❌ No ✅Yes
Applicable
scenarios		 High-performance
LLM reasoning,
API deployment		 Local LLM
operation,
lightweight
reasoning		 Multi-step
reasoning
orchestration,
distributed
computing		 Hugging Face
ecosystem API
deployment		 Low-end device
reasoning,
embedded

6 Core Features of vLLM Hosting

High-Performance GPU Server

Equipped with top-level NVIDIA GPUs such as H100 and A100, it supports any AI inference.

Freely Deploy any Model

Fully compatible with the vLLM platform, users can freely choose and deploy models, including: DeepSeek-R1, Gemma 3, Phi-4, and Llama 3.

Full Root/Admin Access

With full root/admin access, you will be able to take full control of your dedicated GPU servers for vLLM very easily and quickly.

Data Privacy and Security

Provide dedicated servers to avoid sharing resources with other users and ensure full control of data.

24/7 Technical Support

7x24 hours online support helps users solve all problems from environment configuration to model optimization.

Customized Service

Based on enterprise needs, we provide customized server configuration and technical consulting services to ensure maximum resource utilization.

Frequently asked questions

vLLM is a high-performance inference engine optimized for running large language models (LLMs) with low latency and high throughput. It is designed for serving models efficiently on GPU servers, reducing memory usage while handling multiple concurrent requests.
To run vLLM efficiently, you'll need:
✅ GPU: NVIDIA GPU with CUDA support (e.g., A6000, A100, H100, 4090)
✅ CUDA: Version 11.8+
✅ GPU Memory: 16GB+ VRAM for small models, 80GB+ for large models (e.g., Llama-70B)
✅ Storage: SSD/NVMe recommended for fast model loading
vLLM supports most Hugging Face Transformer models, including:
✅ Meta’s LLaMA (Llama 2, Llama 3)
✅ DeepSeek, Qwen, Gemma, Mistral, Phi
✅ Code models (Code Llama, StarCoder, DeepSeek-Coder)
✅ MosaicML's MPT, Falcon, GPT-J, GPT-NeoX, and more
🚫 No, vLLM is optimized for GPU inference only. If you need CPU-based inference, use llama.cpp instead.
✅ Yes, vLLM supports multi-GPU inference using tensor-parallel-size.
🚫 No, vLLM is only for inference. For fine-tuning, use PEFT (LoRA), Hugging Face Trainer, or DeepSpeed.
✅ Use --max-model-len to limit context size
✅ Use tensor parallelism (--tensor-parallel-size) for multi-GPU
✅ Enable quantization (4-bit, 8-bit) for smaller models
✅ Run on high-memory GPUs (A100, H100, 4090, A6000)
🟠 Not directly. But you can load quantized models using bitsandbytes or AutoGPTQ before running them in vLLM.

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