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: High-Throughput LLM Inference Server
Looking for the ultimate llm inference server? vLLM is ideal for anyone needing rapid AI inference. In the vLLM vs Ollama comparison, vLLM stands out for enterprise workloads. Get a dedicated GPU for vLLM to securely run LLMs locally with vLLM, and experience high-throughput llm hosting tailored to your models.
Choose Your vLLM Hosting Plans
GPUMart offers the best dedicated GPU for vLLM. Our cost-effective vLLM hosting acts as a powerful LLM inference server, 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- RTX Pro 2000
$Β 99.00/mo
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Order Now- 28GB RAM
- 16 CPU Cores
- 240GB SSD
- 300Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Nvidia RTX Pro 2000
- CUDA Cores: 4,352
- Tensor Cores: 5th Gen
- GPU Memory: 16GB GDDR7
- FP32 Performance: 17 TFLOPSξ
Hot Sale
Professional GPU VPS - A4000
$Β 89.50/mo
50% OFF Recurring (Was $179.00)
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Order Now- 30GB RAM
- 24 CPU Cores
- 320GB SSD
- 300Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Quadro RTX A4000
- CUDA Cores: 6,144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPSξ
Advanced GPU VPS- RTX Pro 4000
$Β 159.00/mo
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Order Now- 60GB RAM
- 24 CPU Cores
- 320GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Nvidia RTX Pro 4000
- CUDA Cores: 8,960
- Tensor Cores: 280
- GPU Memory: 24GB GDDR7
- FP32 Performance: 34 TFLOPSξ
Advanced GPU Dedicated Server - V100
$Β 229.00/mo
1mo3mo12mo24mo
Order Now- 128GB RAM
- GPU: Nvidia V100
- Dual 12-Core E5-2690v3ξ
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbpsξ
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPSξ
Hot Sale
Advanced GPU Dedicated Server - A5000
$Β 191.95/mo
45% OFF Recurring (Was $349.00)
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Order Now- 128GB RAM
- GPU: Nvidia Quadro RTX A5000
- Dual 12-Core E5-2697v2ξ
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbpsξ
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPSξ
Advanced GPU VPS- RTX Pro 5000
$Β 269.00/mo
1mo3mo12mo24mo
Order Now- 60GB RAM
- 24 CPU Cores
- 320GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Nvidia RTX Pro 5000
- CUDA Cores: 14,080
- Tensor Cores: 440
- GPU Memory: 48GB GDDR7
- FP32 Performance: 66.94 TFLOPSξ
Enterprise GPU Dedicated Server - RTX 4090
$Β 409.00/mo
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Order Now- 256GB RAM
- GPU: GeForce RTX 4090
- Dual 18-Core E5-2697v4ξ
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbpsξ
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPSξ
Enterprise GPU Dedicated Server - RTX A6000
$Β 409.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- GPU: Nvidia Quadro RTX A6000
- Dual 18-Core E5-2697v4ξ
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbpsξ
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPSξ
Enterprise GPU VPS- RTX Pro 6000
$Β 479.00/mo
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Order Now- 90GB RAM
- 32 CPU Cores
- 400GB SSD
- 1000Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Nvidia RTX Pro 6000
- CUDA Cores: 24,064
- Tensor Cores: 852
- GPU Memory: 96GB GDDR7
- FP32 Performance: 126 TFLOPSξ
Enterprise GPU Dedicated Server - A40
$Β 409.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- GPU: Nvidia A40
- Dual 18-Core E5-2697v4ξ
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbpsξ
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 37.48 TFLOPSξ
Hot Sale
Enterprise GPU Dedicated Server - A100
$Β 359.55/mo
55% OFF Recurring (Was $799.00)
1mo3mo12mo24mo
Order Now- 256GB RAM
- GPU: Nvidia A100
- Dual 18-Core E5-2697v4ξ
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbpsξ
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPSξ
Enterprise GPU Dedicated Server - A100(80GB)
$Β 1559.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- GPU: Nvidia A100
- Dual 18-Core E5-2697v4ξ
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbpsξ
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 80GB HBM2e
- FP32 Performance: 19.5 TFLOPSξ
DeepSeek Benchmark Summary
| Model Name | Size (GB)(16-bit Quantization) | Recommended GPU(s) | Concurrent Requests | Tokens/s |
| DeepSeek-R1-Distill-Qwen-1.5B | 3.4β3.6 | A5000 / RTX 4090 | 50 | 3935β8266 |
| DeepSeek-R1-Distill-Qwen-7B | 15 | A5000 / RTX 4090 | 50 | 1604β3965 |
| DeepSeek-R1-Distill-Qwen-14B | 28 | 2ΓRTX 4090 / A100 80GB | 50 | 727β874 |
| DeepSeek-R1-Distill-Qwen-32B | 62 | 2ΓA100 | 50 | 939β2239 |
| DeepSeek-R1-Distill-Llama-8B | 15β16.1 | A5000 / RTX 4090 | 50 | 1514β6882 |
| DeepSeek-R1-Distill-Llama-70B | 132 | 4ΓA6000 | 50 | 466 |
| deepseek-moe-16b-base | 31 | 2ΓRTX 4090 / A100 40GB | 50 | 465β493 |
Gemma Benchmark Summary
| Model Name | Size (GB)(16-bit Quantization) | Recommended GPU(s) | Concurrent Requests | Tokens/s |
| gemma-2-9b-it | 18 | A5000 / A100 40GB | 50 | 951β3003 |
| gemma-2-27b-it | 51 | A100 80GB / 2ΓA100 | 50 | 495β2305 |
| gemma-3-4b-it | 8.1 | A100 40GB / RTX 4090 | 50 | 3976 |
| gemma-3-12b-it | 23 | A100 40GB / RTX 4090 | 30β50 | 400β477 |
| gemma-3-27b-it | 51 | 2ΓA100 80GB | 50 | 1231β2305 |
Qwen Benchmark Summary
Llama Benchmark Summary
vLLama GPU Benchmarks β Model Performance
We've benchmarked LLMs on GPUs includingA5000, T1000, A40, A6000,RTX 4090,Dual RTX 4090, A100 40GB, Dual A100,4xA100 3xV100,A100 80GB,H100, ,4xA6000. Explore the results to select the ideal GPU server for your workload.
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 OpenAIβs 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 β 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 vLLM official Quickstart
vLLM Quick-Start Guides
Use our high-performance GPU servers to run vLLM efficiently. These guides help you install, benchmark, and optimize vLLM for local deployment, inference, or distributed setups.
vLLM Installation & Usage
Master the basics of setting up your own LLM inference server. Follow our comprehensive guide to deploy and configure vLLM efficiently.
π How to Install and Use vLLM
π How to Install and Use vLLM
Offline Inference Benchmark
Evaluate the speed of your dedicated GPU for vLLM. Discover how to test offline throughput for maximum AI efficiency.
π How to Benchmark vLLM Offline Inference
π How to Benchmark vLLM Offline Inference
Online Serving Benchmark
Test and optimize your server for high-throughput LLM hosting. Learn to handle concurrent requests in production environments.
π How to Benchmark vLLM Online Serving
π How to Benchmark vLLM Online Serving
Inference Engines Comparison
Compare top inference engines to find the best fit. See why developers choose to securely run LLMs locally with vLLM.
π SGLang vs vLLM: A Comprehensive Comparison
π SGLang vs vLLM: A Comprehensive Comparison
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 | β Yes | β Yes | β Yes | β Yes | β No |
| Streaming | β Yes | β Yes | β Yes | β Yes | β Yes |
| API Server | β Yes | β Yes | β Yes | β Yes | β No |
| Memory Efficient | β Yes | β Yes | β Yes | β No | β 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 |
vLLM Hosting FAQs: Your LLM Inference Server Guide
Have questions about deployment? Learn about hardware requirements, the vLLM vs Ollama comparison, and how to securely run LLMs locally with vLLM on dedicated GPUs.
What is vLLM?
vLLM is a high-performance llm inference server engine optimized for running large language models (llms ai) with low latency. It enables high-throughput llm processing, designed for serving models efficiently on GPU servers, reducing memory usage while handling multiple concurrent requests.
vLLM vs Ollama: Which one should I choose?
When comparing vLLM vs Ollama, vLLM is designed as a high-throughput llm inference server for production environments and heavy concurrent requests. Ollama is highly user-friendly for local testing on personal machines. If you want to deploy AI models at scale, a dedicated GPU for vLLM is the better choice.
What are the hardware requirements for hosting vLLM?
To run LLMs locally with vLLM efficiently, you'll need a dedicated GPU for vLLM.
β GPU: NVIDIA GPU with CUDA support (e.g., A6000, A100, H100, 4090)
β CUDA: Version 11.8+
β GPU Memory: 16GB+ VRAM for small models, 80GB+ for large models (e.g., Llama 3 70B)
β Storage: SSD/NVMe recommended for fast model loading
β GPU: NVIDIA GPU with CUDA support (e.g., A6000, A100, H100, 4090)
β CUDA: Version 11.8+
β GPU Memory: 16GB+ VRAM for small models, 80GB+ for large models (e.g., Llama 3 70B)
β Storage: SSD/NVMe recommended for fast model loading
What models does vLLM support?
vLLM supports most Hugging Face Transformer models, including:
β Metaβs LLaMA (Llama 2, Llama 3)
β DeepSeek, Qwen, Gemma, Mistral, Phi
β Code models (Code Llama, StarCoder, DeepSeek-Coder)
β MosaicML's MPT, Falcon, GPT-J, GPT-NeoX, and more
β Metaβs LLaMA (Llama 2, Llama 3)
β DeepSeek, Qwen, Gemma, Mistral, Phi
β Code models (Code Llama, StarCoder, DeepSeek-Coder)
β 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?
β
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?
β
Use --max-model-len to limit context size
β Use tensor parallelism (--tensor-parallel-size) for multi-GPU
β Enable quantization (4-bit, 8-bit) for smaller models
β Run on high-memory GPUs (A100, H100, 4090, A6000)
β Use tensor parallelism (--tensor-parallel-size) for multi-GPU
β Enable quantization (4-bit, 8-bit) for smaller models
β 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.