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LLM Inference Server

vLLM Hosting
High-Throughput
Inference at Scale

Deploy your own LLM inference server with a dedicated GPU for vLLM. Run LLMs locally with vLLM — secure, blazing fast, enterprise-grade. GPU memory from 16 GB to 160 GB.

Linux & Windows OS

Full Root / Admin Access

SSH / RDP Access

24/7/365 Expert Support

Ready in 10–40 Minutes

Deploy vLLM Server View Benchmarks

8,266

Tokens/s (peak)

50+

Concurrent Requests

160 GB

Max GPU VRAM

99.9%

Uptime Guarantee

NVIDIA A100 / H100 / RTX 4090

Deploy Your Own vLLM & LLM API

Multi-GPU Support

Self-hosted & Secure

7+ Years Experience

Choose Your vLLM Hosting Plan

GPU memory must be ≥ 1.2× your model size. Select based on your model's parameter count.

Hot Sale

Professional GPU VPS- RTX Pro 2000

$ 95.20/mo

20% OFF Recurring (Was $119.00)

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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 TFLOPS

This is a high-demand pre-order product. Delivery will be completed within 2–7 days after payment.

Professional GPU VPS - A4000

$ 119.00/mo

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28GB 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 TFLOPS

Advanced GPU VPS- RTX Pro 4000

$ 159.00/mo

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56GB 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 TFLOPS

Advanced GPU Dedicated Server - V100

$ 229.00/mo

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128GB RAM
GPU: Nvidia V100
Dual 12-Core E5-2690v3
240GB SSD + 2TB SSD
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Volta
CUDA Cores: 5,120
Tensor Cores: 640
GPU Memory: 16GB HBM2
FP32 Performance: 14 TFLOPS

Advanced GPU Dedicated Server - A5000

$ 269.00/mo

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

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 8192
Tensor Cores: 256
GPU Memory: 24GB GDDR6
FP32 Performance: 27.8 TFLOPS

Advanced GPU VPS- RTX Pro 5000

$ 269.00/mo

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56GB 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 TFLOPS

Enterprise GPU Dedicated Server - RTX 4090

$ 409.00/mo

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256GB RAM
GPU: GeForce RTX 4090
Dual 18-Core E5-2697v4
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
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 TFLOPS

Enterprise GPU Dedicated Server - RTX A6000

$ 409.00/mo

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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: Windows / Linux

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 10,752
Tensor Cores: 336
GPU Memory: 48GB GDDR6
FP32 Performance: 38.71 TFLOPS

Enterprise GPU VPS- RTX Pro 6000

$ 479.00/mo

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84GB 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 TFLOPS

Hot Sale

Enterprise GPU Dedicated Server - A40

$ 296.46/mo

46% OFF Recurring (Was $549.00)

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256GB RAM
GPU: Nvidia A40
Dual 18-Core E5-2697v4
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 10,752
Tensor Cores: 336
GPU Memory: 48GB GDDR6
FP32 Performance: 37.48 TFLOPS

Hot Sale

Enterprise GPU Dedicated Server - A100

$ 359.55/mo

55% OFF Recurring (Was $799.00)

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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 Specifications:
Microarchitecture: Ampere
CUDA Cores: 6912
Tensor Cores: 432
GPU Memory: 40GB HBM2
FP32 Performance: 19.5 TFLOPS

Enterprise GPU Dedicated Server - A100(80GB)

$ 1559.00/mo

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Order Now

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

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 6912
Tensor Cores: 432
GPU Memory: 80GB HBM2e
FP32 Performance: 19.5 TFLOPS

vLLM GPU Benchmark Results

Real-world test data from our own servers — token throughput across DeepSeek, Gemma, Qwen, and Llama models at 50 concurrent requests. Higher is better. Click any bar to see full details.

DeepSeek — Tokens/s at 50 Concurrent Requests (16-bit, vLLM server)

Gemma — Tokens/s at 50 Concurrent Requests (16-bit, vLLM server)

Qwen — Tokens/s at 50 Concurrent Requests (16-bit, vLLM server)

Llama — Tokens/s at 50 Concurrent Requests (16-bit, vLLM server)

Detailed Benchmark Reports

Our in-house benchmark data across individual GPU configurations — single and multi-card setups.

Dedicated · 24 GB

A5000 Benchmark Report

RTX A6000 Benchmark Report

View full results

Dedicated · 24 GB

RTX 4090 Benchmark Report

View full results

Multi-GPU · 2× 24 GB

2× RTX 4090 Benchmark Report

View full results

Dedicated · 40 GB

A100 (40 GB) Benchmark Report

View full results

Dedicated · 16 GB

V100 Benchmark Report

View full results

Dedicated · 80 GB

A100 (80 GB) Benchmark Report

View full results

Dedicated · 80 GB

H100 Benchmark Report

View full results

Multi-GPU · 2× 40 GB

2× A100 (40 GB) Benchmark Report

View full results

Multi-GPU · 4× 40 GB

4× A100 (40 GB) Benchmark Report

View full results

Multi-GPU · 4× 48 GB

4× A6000 Benchmark Report

View full results

Key Features of vLLM

vLLM is an optimized inference engine for serving large language models with high throughput and low latency, designed to maximize GPU utilization.

PagedAttention

A novel memory management technique that improves inference efficiency, allowing faster and more memory-efficient generation with reduced fragmentation.

High-Throughput Serving

Continuous batching of incoming requests with optimized scheduling — maximizing GPU utilization across all concurrent users.

Streaming Support

Real-time token streaming identical to OpenAI's GPT API — drop-in replacement for existing applications.

Multi-GPU Support

Tensor parallelism across multiple GPUs via --tensor-parallel-size — run 70B+ models across 2× or 4× GPU configurations.

OpenAI-Compatible API

Serves models in the exact same API format as OpenAI — integrate with any existing application without code changes.

Efficient KV Cache

Continuous batching with smart KV cache management eliminates memory fragmentation for sustained high-throughput inference.

vLLM vs Ollama vs SGLang vs TGI vs Llama.cpp

Which LLM inference engine is right for you? vLLM leads for production-grade API deployments.

Feature	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
Best for	High-perf API deployment	Local / personal use	Distributed computing	HuggingFace ecosystem	Embedded / low-end

Deploy Your vLLM API Server in 10 Minutes

From bare-metal GPU server to a running OpenAI-compatible endpoint in four steps.

Order GPU Server

Select your plan above and deploy. Server delivered in 10–40 minutes with full root access.

Install vLLM

SSH into your server and install vLLM via pip or uv. Python 3.9–3.12 required on Linux.

Run vLLM Server

Start the OpenAI-compatible server with your chosen model. Default port 8000.

Chat with Model

Query your endpoint via curl, Python client, or any OpenAI-compatible app or SDK.

Requirements

Linux

Python

3.9 – 3.12

GPU Compute

7.0+ (V100, T4, A100, H100…)

Install vLLM via pip or uv

# Create environment
conda create -n vllm python=3.12 -y
conda activate vllm

# Install vLLM
pip install vllm

# Start OpenAI-compatible server
vllm serve Qwen/Qwen2.5-1.5B-Instruct

Need more detail? See the vLLM official quickstart →

vLLM Quick-Start Guides

Step-by-step tutorials to install, benchmark, and optimize vLLM on our GPU servers — from first deploy to production tuning.

vLLM Installation & Usage

Master the basics of setting up your own LLM inference server. Deploy and configure vLLM efficiently from scratch.

How to Install and Use vLLM

Offline Inference Benchmark

Evaluate the speed of your dedicated GPU for vLLM. Test offline throughput for maximum AI efficiency.

How to Benchmark vLLM Offline

Online Serving Benchmark

Test and optimize your server for high-throughput LLM hosting. Handle concurrent requests in production environments.

How to Benchmark vLLM Online

SGLang vs vLLM Comparison

Compare top inference engines to find the best fit for your use case. Deep-dive into performance characteristics.

SGLang vs vLLM: Full Comparison

6 Reasons to Choose Our vLLM Hosting

Enterprise-grade GPU infrastructure on raw bare-metal hardware — no noisy neighbors, no complex pricing.

NVIDIA GPU Selection

Rich GPU lineup from 16 GB to 160 GB VRAM — RTX 4090, A100, H100, A40, A6000, and more. Multi-GPU NVLink options available.

SSD-Based Storage

Intel Xeon processors paired with terabytes of SSD storage and up to 256 GB RAM for fast model loading and low I/O latency.

Full Root / Admin Access

Complete control over your dedicated GPU server via SSH or RDP — install any software, configure any port, your environment.

99.9% Uptime SLA

Enterprise-class data centers with redundant power and network — guaranteed 99.9% uptime for your vLLM inference server.

Dedicated IP Address

Every plan includes a dedicated IPv4 & IPv6 address — expose your vLLM API endpoint directly with no shared IP restrictions.

24/7/365 Expert Support

Round-the-clock technical support from GPU infrastructure specialists — free for all plans, available 365 days a year.

vLLM Hosting FAQs

Common questions about hardware requirements, vLLM vs Ollama, and deploying your LLM inference server.

What is vLLM?

vLLM is a high-performance LLM inference server engine optimized for running large language models 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 via PagedAttention.

vLLM vs Ollama: which should I choose?

vLLM is designed as a high-throughput LLM inference server for production environments and heavy concurrent requests. Ollama is user-friendly for local testing on personal machines. For deploying AI models at scale with an API, a dedicated GPU for vLLM is the better choice — Ollama is not built for multi-user production load.

What are the hardware requirements for vLLM hosting?

To run LLMs locally with vLLM efficiently, you need: NVIDIA GPU with CUDA support (A6000, A100, H100, RTX 4090, etc.), CUDA 11.8+, at least 16 GB VRAM for small models and 80 GB+ for large models like Llama 3 70B, SSD/NVMe storage for fast model loading, and Linux OS with Python 3.9–3.12. GPU memory must be ≥ 1.2× your model size.

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 Llama, StarCoder, DeepSeek-Coder, MosaicML's MPT, Falcon, GPT-J, GPT-NeoX, and many more.

Can I run vLLM on CPU?

No — vLLM is optimized for GPU inference only (CUDA compute capability 7.0+). For CPU-based inference, use llama.cpp instead. For production workloads with real users, a dedicated GPU for vLLM is strongly recommended.

Does vLLM support multiple GPUs?

Yes — vLLM supports multi-GPU inference via tensor parallelism using the --tensor-parallel-size flag. Our multi-GPU servers (2× RTX 4090, 2× A100, 4× A6000, etc.) are purpose-built for large 70B+ model deployments with vLLM.

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 VRAM footprint; run on high-memory GPUs (A100, H100, RTX 4090, A6000). See our benchmark guides for GPU-specific tuning recommendations.

Can I fine-tune models using vLLM?

No — vLLM is an inference-only engine. For fine-tuning, use PEFT (LoRA), Hugging Face Trainer, or DeepSpeed. Once fine-tuned, your model can be served with vLLM for high-throughput production inference.

Does vLLM support model quantization?

Not natively for the quantization step — but you can load pre-quantized models using bitsandbytes or AutoGPTQ before running them in vLLM. vLLM also supports GPTQ and AWQ quantized models natively for reduced VRAM usage.

Start Your vLLM Server Today

Deploy a dedicated GPU server for vLLM in under 40 minutes. Full root access, OpenAI-compatible API, 24/7 expert support included.

Choose a Plan View All GPU Servers

vLLM Hosting High-ThroughputInference at Scale

Choose Your vLLM Hosting Plan

vLLM GPU Benchmark Results

Detailed Benchmark Reports

Key Features of vLLM

PagedAttention

High-Throughput Serving

Streaming Support

Multi-GPU Support

OpenAI-Compatible API

Efficient KV Cache

vLLM vs Ollama vs SGLang vs TGI vs Llama.cpp

Deploy Your vLLM API Server in 10 Minutes

vLLM Quick-Start Guides

vLLM Installation & Usage

Offline Inference Benchmark

Online Serving Benchmark

SGLang vs vLLM Comparison

6 Reasons to Choose Our vLLM Hosting

NVIDIA GPU Selection

SSD-Based Storage

Full Root / Admin Access

99.9% Uptime SLA

Dedicated IP Address

24/7/365 Expert Support

vLLM Hosting FAQs

Start Your vLLM Server Today

vLLM Hosting
High-Throughput
Inference at Scale