High-Performance GPU Server
Equipped with top-level NVIDIA GPUs such as H100 and A100, it supports any AI inference.
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
Cloud Clusters 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.
6 Core Features of vLLM Hosting
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.
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.
| 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 |
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
✅ 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
What models does vLLM support?
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
✅ 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
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)
✅ 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.