Qwen Hosting

Qwen Hosting: Deploy Qwen 1B–72B (VL/AWQ/Instruct) Models Efficiently

 

Qwen Hosting optimizes server environments for deploying and running Qwen series large language models developed by Alibaba. These models, such as Qwen-7B, Qwen-32B, and Qwen-72B, are widely used in natural language processing (NLP), chatbots, code generation, and research applications. Qwen Hosting includes high-performance GPU servers with sufficient VRAM, fast storage (NVMe SSDs), and support for inference frameworks like vLLM, Transformers, or DeepSpeed.

 

Qwen Hosting with Ollama — GPU Recommendation

Qwen Hosting with Ollama provides a streamlined environment for running Qwen large language models using the Ollama framework — a user-friendly platform that simplifies local LLM deployment and inference.
Model NameSize (4-bit Quantization)Recommended GPUsTokens/s
qwen3:0.6b523MBP1000~54.78
qwen3:1.7b1.4GBP1000 < T1000 < GTX1650 < GTX1660 < RTX206025.3-43.12
qwen3:4b2.6GBT1000 < GTX1650 < GTX1660 < RTX2060 < RTX506026.70-90.65
qwen2.5:7b4.7GBT1000 < RTX3060 Ti < RTX4060 < RTX506021.08-62.32
qwen3:8b5.2GBT1000 < RTX3060 Ti < RTX4060 < A4000 < RTX506020.51-62.01
qwen3:14b9.3GBA4000 < A5000 < V10030.05-49.38
qwen3:30b19GBA5000 < RTX4090 < A100-40gb < RTX509028.79-45.07
qwen3:32b
qwen2.5:32b20GBA5000 < RTX4090 < A100-40gb < RTX509024.21-45.51
qwen2.5:72b47GB2*A100-40gb < A100-80gb < H100 < 2*RTX509019.88-24.15
qwen3:235b142GB4*A100-40gb < 2*H100~10-20

Qwen Hosting with vLLM + Hugging Face — GPU Recommendation

Qwen Hosting with vLLM + Hugging Face delivers an optimized server environment for running Qwen large language models using the high-performance vLLM inference engine, seamlessly integrated with the Hugging Face Transformers ecosystem.
Model NameSize (16-bit Quantization)Recommended GPU(s)Concurrent RequestsTokens/s
Qwen/Qwen2-VL-2B-Instruct~5GBA4000 < V10050~3000
Qwen/Qwen2.5-VL-3B-Instruct~7GBA5000 < RTX4090502714.88-6980.31
Qwen/Qwen2.5-VL-7B-Instruct,
Qwen/Qwen2-VL-7B-Instruct~15GBA5000 < RTX4090501333.92-4009.29
Qwen/Qwen2.5-VL-32B-Instruct,
Qwen/Qwen2.5-VL-32B-Instruct-AWQ~65GB2*A100-40gb < H10050577.17-1481.62
Qwen/Qwen2.5-VL-72B-Instruct,
Qwen/QVQ-72B-Preview,
Qwen/Qwen2.5-VL-72B-Instruct-AWQ~137GB4*A100-40gb < 2*H100 < 4*A600050154.56-449.51

Express GPU Dedicated Server - P1000

Best For College Project

$74/mo
    • 32 GB RAM
    • GPU: Nvidia Quadro P1000
    • Eight-Core Xeon E5-2690
    • 120GB + 960GB SSD
    • 100Mbps-1Gbps
    • OS: Windows / Linux

Basic GPU Dedicated Server - T1000

For business

$109/mo
    • 64 GB RAM
    • GPU: Nvidia Quadro T1000
    • Eight-Core Xeon E5-2690
    • 120GB + 960GB SSD
    • 100Mbps-1Gbps
    • OS: Windows / Linux

Basic GPU Dedicated Server - GTX 1650

For business

$129/mo
  • 64GB RAM
  • GPU: Nvidia GeForce GTX 1650
  • Eight-Core Xeon E5-2667v3
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Basic GPU Dedicated Server - GTX 1660

For business

$149/mo
  • 64GB RAM
  • GPU: Nvidia GeForce GTX 1660
  • Dual 10-Core Xeon E5-2660v2
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - V100

Best For College Project

$239/mo
  • 128GB RAM
  • GPU: Nvidia V100
  • Dual 12-Core E5-2690v3
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Professional GPU Dedicated Server - RTX 2060

For business

$209/mo
  • 128GB RAM
  • GPU: Nvidia GeForce RTX 2060
  • Dual 10-Core E5-2660v2
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - RTX 2060

For business

$249/mo
  • 128GB RAM
  • GPU: Nvidia GeForce RTX 2060
  • Dual 20-Core Gold 6148
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - RTX 3060 Ti

For business

$249/mo
  • 128GB RAM
  • GPU: GeForce RTX 3060 Ti
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Professional GPU VPS - A4000

For Business

$139/mo
  • 32GB RAM
  • 24 CPU Cores
  • 320GB SSD
  • 300Mbps Unmetered Bandwidth
  • Once per 2 Weeks Backup
  • OS: Linux / Windows 10/ Windows 11

Advanced GPU Dedicated Server - A4000

For business

$289/mo
  • 128GB RAM
  • GPU: Nvidia Quadro RTX A4000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - A5000

For business

$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 - A40

For business

$449/mo
  • 256GB RAM
  • GPU: Nvidia A40
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Basic GPU Dedicated Server - RTX 5060

For Business

$199/mo
  • 64GB RAM
  • GPU: Nvidia GeForce RTX 5060
  • 24-Core Platinum 8160
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - RTX 5090

For business

$489/mo
  • 256GB RAM
  • GPU: GeForce RTX 5090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A100

For business

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

Enterprise GPU Dedicated Server - A100(80GB)

For business

$1569/mo
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - H100

For Business

$2109/mo
  • 256GB RAM
  • GPU: Nvidia H100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server- 2xRTX 4090

For business

$739/mo
  • 256GB RAM
  • GPU: 2 x GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server- 2xRTX 5090

For business

$869/mo
  • 256GB RAM
  • GPU: 2 x GeForce RTX 5090
  • Dual Gold 6148
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xA100

For business

$1309/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 - 2xRTX 3060 Ti

For Business

$329/mo
  • 128GB RAM
  • GPU: 2 x GeForce RTX 3060 Ti
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX 4060

For business

$279/mo
  • 64GB RAM
  • GPU: 2 x Nvidia GeForce RTX 4060
  • Eight-Core E5-2690
  • 120GB SSD + 960GB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX A5000

For business

$449/mo
  • 128GB RAM
  • GPU: 2 x Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX A4000

For business

$369/mo
  • 128GB RAM
  • GPU: 2 x Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX 3060 Ti

For Business

$379/mo
  • 256GB RAM
  • GPU: 3 x GeForce RTX 3060 Ti
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xV100

For business

$479/mo
  • 256GB RAM
  • GPU: 3 x Nvidia V100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX A5000

For business

$549/mo
  • 256GB RAM
  • GPU: 3 x Quadro RTX A5000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX A6000

For business

$909/mo
  • 256GB RAM
  • GPU: 3 x Quadro RTX A6000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 4xA100

For Business

$1909/mo
  • 512GB RAM
  • GPU: 4 x Nvidia A100
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 4xRTX A6000

For business

$1209/mo
  • 512GB RAM
  • GPU: 4 x Quadro RTX A6000
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 8xV100

For business

$1509/mo
  • 512GB RAM
  • GPU: 8 x Nvidia Tesla V100
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 8xRTX A6000

For business

$2109/mo
  • 512GB RAM
  • GPU: 8 x Quadro RTX A6000
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
qwen-1

What is Qwen Hosting?

 

Qwen Hosting refers to server hosting environments specifically optimized to run the Qwen family of large language models, developed by Alibaba Cloud (AliNLP). These models — such as Qwen-7B, Qwen-14B, Qwen-72B, and distilled variants like Qwen-1.5B — are open-source LLMs designed for tasks like text generation, question answering, dialogue, and code understanding.

 

Qwen Hosting provides the hardware (typically high-end GPUs) and software stack (inference frameworks like vLLM, Transformers, or Ollama) necessary to deploy, run, fine-tune, and scale these models in production or research settings.

LLM Benchmark Test Results for Qwen 3/2.5/2 Hosting

This benchmark report provides detailed performance evaluations of hosting Qwen-3, Qwen-2.5, and Qwen-2 large language models across a range of GPU environments.
ollama-benchmark
Read more

Ollama Benchmark for Qwen

This benchmark report evaluates the performance of Qwen models running under the Ollama framework, a lightweight and developer-friendly platform for local and cloud-based LLM inference.
vllm-benchmark
Read more

vLLM Benchmark for Qwen

This benchmark evaluates the performance of Qwen large language models running on the vLLM inference engine, designed for high-throughput, low-latency LLM serving. vLLM leverages PagedAttention and continuous batching, making it ideal for deploying Qwen models in real-time applications such as chatbots, AI assistants, and developer APIs.

How to Deploy Qwen LLMs with Ollama/vLLM

    Install and Run Qwen Locally with Ollama >

    Ollama is a self-hosted AI solution to run open-source large language models, such as DeepSeek, Gemma, Llama, Mistral, and other LLMs locally or on your own infrastructure.

    Install and Run Qwen Locally with vLLM v1 >

    vLLM is an optimized framework designed for high-performance inference of Large Language Models (LLMs). It focuses on fast, cost-efficient, and scalable serving of LLMs.

    What Does Qwen Hosting Stack Include?

    llama-2

    Hardware Stack

    ✅ GPU: NVIDIA RTX 4090 / 5090 / A100 / H100 (depending on model size)

    ✅GPU Count: 1–8 GPUs for multi-GPU hosting (Qwen-72B or Qwen2/3 with 100B+ params)

    ✅CPU: 16–64 vCores (e.g., AMD EPYC / Intel Xeon)

    ✅RAM: 64GB–512GB system memory (depends on parallelism & model size)

    ✅Storage: NVMe SSD (1TB or more, for model weights and checkpoints)

    ✅Networking: 1 Gbps (for API usage or streaming tokens at low latency)

    qwen

    Software Stack

    ✅ OS: Ubuntu 20.04 / 22.04 (preferred for ML compatibility)

    ✅ Drivers: NVIDIA GPU Driver (latest stable), CUDA Toolkit (e.g., CUDA 11.8 / 12.x)

    ✅Runtime: cuDNN, NCCL, and Python (3.9 or 3.10)

    ✅ Inference Engine: vLLM, Ollama, Transformers

    ✅ Model Format: Qwen models in Hugging Face format (.safetensors, .bin, or GGUF for quantized versions)

    ✅ API Server: FastAPI / Flask / OpenAI-compatible server wrapper (for inference endpoints)

    ✅ Containerization: Docker (optional, for deployment & reproducibility)

    ✅ Optional Tools: Triton Inference Server, DeepSpeed, Hugging Face Text Generation Inference (TGI), LMDeploy

    Why Qwen Hosting Needs a Specialized Hardware + Software Stack

    Hosting Qwen models — such as Qwen-1.5B, Qwen-7B, Qwen-14B, or Qwen-72B — requires a carefully designed hardware + software stack to ensure fast, scalable, and cost-efficient inference. These models are powerful but resource-intensive, and standard infrastructure often fails to meet their performance and memory requirements.

    Qwen Models Are Large and Memory-Hungry

    When deploying Qwen series large language models (such as Qwen-7B, Qwen-14B or Qwen-72B), general-purpose servers and software stacks often cannot meet their high memory and high computing power operation requirements. Even Qwen-7B requires a GPU with at least 24GB of video memory for smooth reasoning, while larger models such as Qwen-72B require multiple cards in parallel.

    Throughput & Latency Optimization

    In addition to hardware requirements, Qwen reasoning also requires specialized reasoning engine support, such as vLLM, DeepSpeed, Ollama or Hugging Face Transformers. These engines provide efficient batch processing, paged attention (PagedAttention), streaming response and other functions, which can greatly improve the response speed and system stability when multiple users are concurrent.

    Software Stack Needs to Be LLM-Optimized

    At the software level, Qwen Hosting also relies on a complete set of LLM optimization tool chains, including CUDA, cuDNN, NCCL, PyTorch, and a runtime environment that supports quantization (such as INT4, AWQ). The system also needs to deploy a high-performance tokenizer, OpenAI-compatible API interface, and a memory scheduler for model management and context caching.

    Infrastructure Must Support Large-Scale Serving

    Qwen Hosting is not a task that general-purpose cloud hosts can handle. It requires customized GPU hardware configuration, combined with advanced LLM inference framework and optimized software stack to meet the stringent requirements of modern AI applications in terms of response speed, concurrent processing and deployment efficiency. This is why a dedicated ‘hardware + software’ combination must be adopted to deploy the Qwen model.

    Self-hosted Qwen Hosting vs. Qwen as a Service

    In addition to GPU-based dedicated servers that host LLM models themselves, there are also many LLM API (Large Model as a Service) solutions on the market, which have become one of the mainstream ways to use models.
    Feature / Aspect🖥️ Self-hosted Qwen Hosting☁️ Qwen as a Service
    Control & OwnershipFull control over model weights, deployment environment, and accessManaged by provider; limited access and customization
    Deployment TimeRequires setup of hardware, environment, and inference stackReady to use instantly via API; minimal setup required
    Performance OptimizationCan fine-tune inference stack (vLLM, Triton, quantization, batching)Limited ability to optimize or change backend stack
    ScalabilityFully scalable with multi-GPU, local clusters, or on-prem setupsConstrained by provider quotas, pricing tiers, and throughput
    Cost StructureHigher upfront (GPU server + setup), lower long-term cost per tokenPay-per-use; cost grows quickly with high-volume usage
    Data Privacy & SecurityRuns in private or on-prem environment; full control of dataData must be sent to external service; potential compliance risk
    Model FlexibilityDeploy any Qwen variant (7B, 14B, 72B, etc.), quantized or fine-tunedLimited to what provider offers; usually fixed model versions
    Use Case FitIdeal for enterprises, AI startups, researchers, privacy-critical appsBest for prototyping, low-volume use, fast product experiments

    FAQs: Qwen 1B–72B (VL / AWQ / Instruct) Models Hosting

    What types of Qwen models can be hosted?

    We support hosting for the full Qwen model family, including:

    • Base Models: Qwen-1B, 7B, 14B, 72B
    • Instruction-Tuned Models: Qwen-1.5-Instruct, Qwen2-Instruct, Qwen3-Instruct
    • Quantized Models: AWQ, GPTQ, INT4/INT8 variants
    • Multimodal Models: Qwen-VL and Qwen-VL-Chat

    Which inference backends are supported?

    We support multiple deployment stacks, including:

    • vLLM (preferred for high-throughput & streaming)
    • Ollama (fast local development)
    • Hugging Face Transformers + Accelerate / Text Generation Inference
    • DeepSpeed, TGI, and LMDeploy for fine-tuned control and optimization

    Can I host Qwen models with quantization (AWQ / GPTQ)?

    Yes. We support quantized Qwen variants (like AWQ, GPTQ, INT4) using optimized inference engines such as vLLM with AWQ support, AutoAWQ, and LMDeploy. This allows large models to run on fewer or lower-end GPUs.

    Is multi-user API access available?

    Yes. We offer OpenAI-compatible API endpoints for shared usage, including support for:

    • API key management
    • Rate limiting
    • Streaming (/v1/chat/completions)
    • Token counting & usage tracking

    Do you support custom fine-tuned Qwen models?

    Yes. You can deploy your own fine-tuned or LoRA-adapted Qwen checkpoints, including adapter_config.json and tokenizer files.

    What’s the difference between Instruct, VL, and Base Qwen models?

    • Base: Raw pretrained models, ideal for continued training
    • Instruct: Instruction-tuned for chat, Q&A, reasoning
    • VL (Vision-Language): Supports image + text input/output

    Can I deploy Qwen in a private environment or on-premises?

    Yes. We support self-hosted deployments (air-gapped or hybrid), including configuration of local inference stacks and model vaults.