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.
Qwen Hosting Service: Deploy Qwen 1B–72B (VL/AWQ/Instruct) Models Efficiently
Qwen Hosting Service 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.
Pre-installed Qwen3-32B LLM Hosting
Cloud Clusters offers best budget GPU servers for Qwen3 LLMs. You'll get pre-installed Open WebUI + Ollama + Qwen3-32B, it is a popluar way to self-hosted LLM models.
Advanced GPU Dedicated Server - A5000
$ 269.00/mo
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Order Now- 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
Enterprise GPU Dedicated Server - RTX 4090
$ 409.00/mo
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Order Now- 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
New Arrival
Advanced GPU VPS - RTX 5090
$ 339.00/mo
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Order Now- 96GB RAM
- 32 CPU Cores
- 400GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- Dedicated GPU: GeForce RTX 5090
- CUDA Cores: 21,760
- Tensor Cores: 680
- GPU Memory: 32GB GDDR7
- FP32 Performance: 109.7 TFLOPS
Enterprise GPU Dedicated Server - A100
$ 639.00/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: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
Ollama Qwen Hosting Service — 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 Name | Size (4-bit Quantization) | Recommended GPUs | Tokens/s |
|---|---|---|---|
| qwen3:0.6b | 523MB | P1000 | ~54.78 |
| qwen3:1.7b | 1.4GB | P1000 < T1000 < GTX1650 < GTX1660 < RTX2060 | 25.3-43.12 |
| qwen3:4b | 2.6GB | T1000 < GTX1650 < GTX1660 < RTX2060 < RTX5060 | 26.70-90.65 |
| qwen2.5:7b | 4.7GB | T1000 < RTX3060 Ti < RTX4060 < RTX5060 | 21.08-62.32 |
| qwen3:8b | 5.2GB | T1000 < RTX3060 Ti < RTX4060 < A4000 < RTX5060 | 20.51-62.01 |
| qwen3:14b | 9.3GB | A4000 < A5000 < V100 | 30.05-49.38 |
| qwen3:30b | 19GB | A5000 < RTX4090 < A100-40gb < RTX5090 | 28.79-45.07 |
| qwen3:32b qwen2.5:32b | 20GB | A5000 < RTX4090 < A100-40gb < RTX5090 | 24.21-45.51 |
| qwen2.5:72b | 47GB | 2*A100-40gb < A100-80gb < H100 < 2*RTX5090 | 19.88-24.15 |
| qwen3:235b | 142GB | 4*A100-40gb < 2*H100 | ~10-20 |
vLLM Qwen Hosting Service — 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 Name | Size (16-bit Quantization) | Recommended GPU(s) | Concurrent Requests | Tokens/s |
|---|---|---|---|---|
| Qwen/Qwen2-VL-2B-Instruct | ~5GB | A4000 < V100 | 50 | ~3000 |
| Qwen/Qwen2.5-VL-3B-Instruct | ~7GB | A5000 < RTX4090 | 50 | 2714.88-6980.31 |
| Qwen/Qwen2.5-VL-7B-Instruct, Qwen/Qwen2-VL-7B-Instruct | ~15GB | A5000 < RTX4090 | 50 | 1333.92-4009.29 |
| Qwen/Qwen2.5-VL-32B-Instruct, Qwen/Qwen2.5-VL-32B-Instruct-AWQ | ~65GB | 2*A100-40gb < H100 | 50 | 577.17-1481.62 |
| Qwen/Qwen2.5-VL-72B-Instruct, Qwen/QVQ-72B-Preview, Qwen/Qwen2.5-VL-72B-Instruct-AWQ | ~137GB | 4*A100-40gb < 2*H100 < 4*A6000 | 50 | 154.56-449.51 |
✅ Explanation:
- Recommended GPUs: From left to right, performance from low to high
- Tokens/s: from benchmark data.
Choose The Best GPU Plans for Qwen 2B-72B Hosting
If the pre-installed product does not meet your needs, you can rent a server and install it yourself—everything under your control.
Professional GPU VPS - A4000
$ 129.00/mo
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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
- Dedicated GPU: Quadro RTX A4000
- CUDA Cores: 6,144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
Advanced GPU Dedicated Server - A5000
$ 269.00/mo
1mo3mo12mo24mo
Order Now- 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
New Arrival
Advanced GPU VPS - RTX 5090
$ 339.00/mo
1mo3mo12mo24mo
Order Now- 96GB RAM
- 32 CPU Cores
- 400GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- Dedicated GPU: GeForce RTX 5090
- CUDA Cores: 21,760
- Tensor Cores: 680
- GPU Memory: 32GB GDDR7
- FP32 Performance: 109.7 TFLOPS
Enterprise GPU Dedicated Server - RTX A6000
$ 409.00/mo
1mo3mo12mo24mo
Order Now- 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
More GPU Hosting Plansarrow_circle_right
What is Qwen Hosting?
Qwen Hosting is a service of 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.
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?
Hosting Qwen models efficiently requires a robust software and hardware stack. A typical Qwen LLM hosting stack includes the following components:
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)
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 & Ownership | Full control over model weights, deployment environment, and access | Managed by provider; limited access and customization |
| Deployment Time | Requires setup of hardware, environment, and inference stack | Ready to use instantly via API; minimal setup required |
| Performance Optimization | Can fine-tune inference stack (vLLM, Triton, quantization, batching) | Limited ability to optimize or change backend stack |
| Scalability | Fully scalable with multi-GPU, local clusters, or on-prem setups | Constrained by provider quotas, pricing tiers, and throughput |
| Cost Structure | Higher upfront (GPU server + setup), lower long-term cost per token | Pay-per-use; cost grows quickly with high-volume usage |
| Data Privacy & Security | Runs in private or on-prem environment; full control of data | Data must be sent to external service; potential compliance risk |
| Model Flexibility | Deploy any Qwen variant (7B, 14B, 72B, etc.), quantized or fine-tuned | Limited to what provider offers; usually fixed model versions |
| Use Case Fit | Ideal for enterprises, AI startups, researchers, privacy-critical apps | Best for prototyping, low-volume use, fast product experiments |
FAQs: Qwen 1B–72B (VL / AWQ / Instruct) Service 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?
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.