NVIDIA GPU
Rich Nvidia graphics card types, up to 160GB VRAM, powerful CUDA performance. There are also multi-card servers for you to choose from.
vLLM Hosting, Exploring vLLM as an Alternative to Ollama
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
GPUMart 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.
Flash Sale to June 16
Professional GPU VPS - A4000
$ 99.00/mo
44% OFF Recurring (Was $179.00)
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Order Now- 32GB RAM
- 24 CPU Cores
- 320GB SSD
- 300Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- Dedicated GPU: Quadro RTX A4000
- CUDA Cores: 6,144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
- Available for Rendering, AI/Deep Learning, Data Science, CAD/CGI/DCC.
Advanced GPU Dedicated Server - V100
$ 229.00/mo
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Order Now- 128GB RAM
- Dual 12-Core E5-2690v3
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia V100
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
- Cost-effective for AI, deep learning, data visualization, HPC, etc
Half-Year Pay, Full-Year Deal
Advanced GPU Dedicated Server - A5000
$ 191.90/mo
45% OFF Recurring (Was $349.00)
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Order Now- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A5000
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Enterprise GPU Dedicated Server - RTX 4090
$ 409.00/mo
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Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 4090
- 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 A6000
$ 409.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Enterprise GPU Dedicated Server - A40
$ 439.00/mo
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Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A40
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 37.48 TFLOPS
- Ideal for hosting AI image generator, deep learning, HPC, 3D Rendering, VR/AR etc.
Enterprise GPU Dedicated Server - A100
$ 639.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
- Good alternativeto A800, H100, H800, L40. Support FP64 precision computation, large-scale inference/AI training/ML.etc
Multi-GPU Dedicated Server - 2xA100
$ 1099.00/mo
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Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
- Free NVLink Included
- A Powerful Dual-GPU Solution for Demanding AI Workloads, Large-Scale Inference, ML Training.etc. A cost-effective alternative to A100 80GB and H100, delivering exceptional performance at a competitive price.
Multi-GPU Dedicated Server - 4xA100
$ 1899.00/mo
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Order Now- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 4 x Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
New Arrival
Enterprise GPU Dedicated Server - A100(80GB)
$ 1559.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 80GB HBM2e
- FP32 Performance: 19.5 TFLOPS
Enterprise GPU Dedicated Server - H100
$ 2099.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia H100
- Microarchitecture: Hopper
- CUDA Cores: 14,592
- Tensor Cores: 456
- GPU Memory: 80GB HBM2e
- FP32 Performance: 183TFLOPS
6 Reasons to Choose our vLLM Hosting
GPUMart enables powerful GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.
SSD-Based Drives
You can never go wrong with our own top-notch dedicated GPU servers for vLLM, loaded with the latest Intel Xeon processors, terabytes of SSD disk space, and up to 256 GB of RAM per server.
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.
99.9% Uptime Guarantee
With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for vLLM hosting service.
Dedicated IP
One of the premium features is the dedicated IP address. Even the cheapest GPU hosting plan is fully packed with dedicated IPv4 & IPv6 Internet protocols.
24/7/365 Technical Support
GPUMart provides round-the-clock technical support to help you resolve any issues related to vLLM hosting.
Key Features of vLLM
vLLM is an optimized inference engine for serving large language models (LLMs) with high throughput and low latency. It is designed to maximize GPU utilization, making it ideal for LLM APIs, chatbots, and other AI applications that require efficient inference.
check_circlePagedAttention
A novel memory management technique that improves inference efficiency, allowing faster and more memory-efficient generation.
check_circleHigh-Throughput Serving
vLLM can batch multiple requests and execute them efficiently, maximizing GPU utilization.
check_circleStreaming Support
Enables real-time token streaming similar to OpenAI’s GPT APIs.
check_circleMulti-GPU Support
Works across multiple GPUs to handle larger models and higher workloads.
check_circleCompatibility with OpenAI API
Can serve models in an API format similar to OpenAI, making it easy to integrate with existing applications.
check_circleEfficient KV Cache Management
Unlike traditional inference engines, vLLM reduces memory fragmentation and supports continuous batching.
Use Cases
vLLM is ideal for anyone needing a high-performance LLM inference engine for large-scale AI applications.
Deploying LLM APIs (e.g., GPT models, LLaMA, Mistral, Gemma, etc.).
Chatbots & Assistants that need real-time response.
High-load applications requiring concurrent requests handling.
Fine-tuned LLM inference for various enterprise applications.
How to deploy a vLLM API server
Deploy vLLM on bare-metal server with a dedicated GPU or Multi-GPU in 10 minutes.
Order and Login GPU Server
Install vLLM
Run vLLM Server with a Model
Chat with the Model
Requirements
OS: Linux
Python: 3.9 – 3.12
GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)
Install vLLM using Python
You can create a new Python environment using conda:
# Create a new conda environment. conda create -n vllm python=3.12 -y conda activate vllm
Or you can create a new Python environment using uv, a very fast Python environment manager. Please follow the documentation to install uv. After installing uv, you can create a new Python environment using the following command:
# (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools` in the environment. uv venv vllm --python 3.12 --seed source vllm/bin/activate
You can install vLLM using either pip or uv pip:
# If you are using pip pip install vllm # If you are using uv uv pip install vllm
Start a OpenAI-Compatible vLLM Server
vLLM can be deployed as a server that implements the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. By default, it starts the server at http://localhost:8000. You can specify the address with --host and --port arguments.
Run the following command to start the vLLM server with the Qwen2.5-1.5B-Instruct model:
vllm serve Qwen/Qwen2.5-1.5B-Instruct
For more help, please refer to the official Quickstart: https://docs.vllm.ai/en/stable/getting_started/quickstart.html
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.