vLLM Hosting

vLLM Hosting, Run LLMs Locally with vLLM

 

vLLM is ideal for anyone needing a high-performance LLM inference engine. Explore vLLM Hosting, where we delve into vLLM as a superior alternative to Ollama. Experience optimized hosting solutions tailored for your needs.

 

Choose Your vLLM Hosting Plans

DBM Mart 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

$139/mo
  • 32GB RAM
  • 24 CPU Cores
  • 320GB SSD
  • 300Mbps Unmetered Bandwidth

Advanced GPU Dedicated Server - A5000

$279/mo
  • 128GB RAM
  • GPU: Nvidia Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - RTX A6000

$559/mo
  • 256GB RAM
  • GPU: Nvidia Quadro RTX A6000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - RTX 4090

$549/mo
  • 256GB RAM
  • GPU: GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A100

$809/mo
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xA100

$1409/mo
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 4xA100

$2509/mo

 

  • 512GB RAM
  • GPU: 4 x Nvidia A100
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A100(80GB)

$1709/mo
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

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
7×24 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.

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.
FeaturesvLLMOllamaSGLangTGI(HF)Llama.cpp
Optimized forGPU (CUDA)CPU/GPU/M1/M2GPU/TPUGPU (CUDA)CPU/ARM
PerformanceHighMediumHighMediumLow
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 scenariosHigh-performance LLM reasoning, API deploymentLocal LLM operation, lightweight reasoningMulti-step reasoning orchestration, distributed computingHugging Face ecosystem API deploymentLow-end device reasoning, embedded

FAQs of vLLM Hosting

Here are some frequently asked questions (FAQs) about vLLM hosting:

What is vLLM?

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.

What are the hardware requirements for hosting vLLM?

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

Can I run vLLM on CPU?

🚫 No, vLLM is optimized for GPU inference only. If you need CPU-based inference, use llama.cpp instead.

Does vLLM support multiple GPUs?

Yes, vLLM supports multi-GPU inference using tensor-parallel-size.

Can I fine-tune models using vLLM?

No, vLLM is only for inference. For fine-tuning, use PEFT (LoRA), Hugging Face Trainer, or DeepSpeed.

How do I optimize vLLM for better performance?

✅ 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)

Does vLLM support model quantization?

Not directly. But you can load quantized models using bitsandbytes or AutoGPTQ before running them in vLLM.