Qwen2.5 Hosting, Host Your
Qwen-2.5 with Ollama

Qwen 2.5 is designed to be a versatile tool, capable of handling a wide range of tasks across various industries. Find the most optimal way to host your own Qwen LLM on our cheap GPU servers.

  • Windows or Linux OS
  • Full Root/Admin Access
  • Support RDP/SSH Access
  • Free 24/7/365 Expert Online Support
  • Servers Delivered within 20 to 40 Minutes

Choose Your Qwen2.5 Hosting Plans

We offer best budget GPU servers for Qwen2.5. Cost-effective dedicated GPU servers are ideal for hosting your own LLMs online.

Professional GPU VPS - A4000


  • 32GB RAM
  • 24 CPU Cores
  • 320GB SSD
  • 300Mbps Unmetered
         Bandwidth





    • Once per 2 Weeks Backup
    OS: Windows / Linux 10/
         Windows11
    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 - A5000

  • 128GB RAM
  • Dual 12-Core E5-2697v2
        (24 Cores & 48 Threads
  • 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 A6000


  • 256GB RAM
  • Dual 18-Core E5-2697v4
        (36 Cores & 72 Threads
  • 240GB SSD + 2TB NVMe+8TB
         SATA
  • 100Mbps-1Gbps



    • OS: Linux / Windows
    GPU: Nvidia Quadro RTX A6000

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


    • Optimally running AI, deep learning, data visualization, HPC, etc.

    Enterprise GPU Dedicated Server - RTX 4090

  • 256GB RAM
  • Dual 18-Core E5-2697v4
         (36 Cores & 72 Threads)
  • 240GB SSD + 2TB NMVe+ 8TB
         SATA
  • 100Mbps-1Gbps




    • OS: Windows / Linux
    GPU: GeForce RTX 4090

  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS


    • Perfect for 3D rendering/modeling , CAD/ professional design, video editing, gaming, HPC, AI/deep learning.

    Enterprise GPU Dedicated Server - A100

  • 256GB RAM
  • Dual 18-Core E5-2697v4
        (36 Cores & 72 Threads)
  • 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

    Enterprise GPU Dedicated Server - A100(80GB)

  • 256GB RAM
  • Dual 18-Core E5-2697v4
        36 Cores & 72 Threads
  • 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









    • Multi-GPU Dedicated Server - 4xRTX A6000

  • 512GB RAM
  • Dual 22-Core E5-2699v4
        (44 Cores & 88 Threads
  • 240GB SSD + 4TB NVMe + 16TB
        SATA
  • 1Gbps


    • OS: Windows / Linux
    GPU: 4xQuadro RTX A6000

  • Microarchitecture: Ampere
  • CUDA Cores: 10,752/li>
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71
        TFLOPS








    • Multi-GPU Dedicated Server - 8xRTX A6000

  • 512GB RAM
  • Dual 22-Core E5-2699v4
        (44 Cores & 88 Threads
  • 240GB SSD + 4TB NVMe + 16TB
        SATA
  • 1Gbps


    • OS: Windows / Linux
    GPU: 8xQuadro RTX A6000

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










    • More GPU Hosting Plans

    6 Reasons to Choose our GPU Servers for Qwen2.5 Hosting

    Infotronics enables powerful GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.

    NVIDIA GPU

    NVIDIA GPU

    Rich Nvidia graphics card types, up to 8x48GB VRAM, powerful CUDA performance. There are also multi-card servers for you to choose from.


    SSD-Based Drives

    SSD-Based Drives

    You can never go wrong with our own top-notch dedicated GPU servers, loaded with the latest Intel Xeon processors, terabytes of SSD disk space, and 256 GB of RAM per server.

    Full Root/Admin Access

    Full Root/Admin Access

    With full root/admin access, you will be able to take full control of your dedicated GPU servers very easily and quickly.

    99.9% Uptime Guarantee

    99.9% Uptime Guarantee

    With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for LLM Hosting service

    Dedicated IP

    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

    24/7/365 Technical Support

    We provides round-the-clock technical support to help you resolve any issues related to DeepSeek hosting.


    Key Features and Capabilities of Qwen 2.5

    Understanding the core strengths of a tool is the first step toward maximizing its potential.

    Expanded Model Range

    Offers a variety of models to suit different applications, with sizes ranging from 0.5 to 72 billion parameters.


    Larger Training Dataset

    It possesses significantly more knowledge and has greatly enhanced capabilities in coding and mathematics, due to specialized expert models in these domains.

    Extended Context Window

    Capable of processing and generating content across multiple formats. It supports long contexts of up to 128K tokens and can generate up to 8K tokens.

    Superior Coding Abilities

    Demonstrates improved coding skills, making it a valuable tool for developers. Enhanced capabilities in mathematical reasoning tasks.


    Multilingual Support

    It offers multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.


    Better Efficiency and Speed

    Utilizes a Mixture of Experts (MoE) architecture, employing 64 specialized expert networks activated dynamically, enhancing efficiency and reducing computational costs by approximately 30% compared to monolithic architectures.

    How to Run Qwen 2.5 LLMs with Ollama

    Let's go through Get up and running with Qwen, DeepSeek, Llama, Gemma, and other LLMs with Ollama step-by-step.



    Order and Login GPU Server



    Download and Install Ollama



    Run Qwen 2.5 with Ollama



    Chat with Qwen 2.5



    FAQs of Qwen2.5 Hosting

    Here are some Frequently Asked Questions (FAQs) related to hosting and deploying the Qwen 2.5 model.

    What is Qwen2.5?
    Qwen2.5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model is a series of advanced AI models developed by Alibaba, including large language models (LLMs), multimodal models, and specialized models for coding (Qwen2.5-Coder) and mathematics (Qwen2.5-Math). It supports up to 128K context length and 29+ languages, making it versatile for various applications.
    GPU Memory: At least 14.74 GiB for smaller models like Qwen2.5-7B. Larger models (e.g., 72B) may require multiple GPUs or 60GB+ VRAM configurations. CPU and RAM: Minimum 8 CPU cores and 32GB RAM for smaller models. Quantization: For resource-constrained environments, consider using quantized versions (e.g., Q4_K_M) to reduce memory usage.
    Yes, Qwen 2.5 can be deployed locally using tools like Ollama or Docker.
    Qwen 2.5 is compatible with multiple frameworks, including: 1. Transformers: For general-purpose inference. 2. vLLM: For high-throughput, low-latency inference. 3. Ollama: For local deployment and API integration. 4. ModelScope: For easy model downloading and fine-tuning.
    Use Open WebUI for a graphical interface to interact with the model. Alternatively, use API endpoints (e.g., /api/generate or /api/chat) for programmatic access.
    Yes, Qwen 2.5 supports fine-tuning using frameworks like Axolotl, Llama-Factory, and ms-swift. Fine-tuning can be done on both local and cloud environments.

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