vLLM Hosting, Run LLMs Locally with vLLM

vLLM is ideal for anyone needing a high-performance LLM inference engine. Explore vLLM Hosting, where we delve into vLLM as a superior alternative to Ollama. Experience optimized hosting solutions tailored for your needs.

  • Top-level GPUs: NVIDIA H100, A100, RTX4090, A6000, etc.
  • Freely deploy any model supported by vLLM
  • Free 24/7/365 Expert Online Support
  • Full Root/Admin Access

Choose Your vLLM Hosting Plans

Infotronics Integrators (I) Pvt. Ltd offers best budget GPU servers for vLLM. Cost-effective vLLM hosting is 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 - A4000


  • 32GB RAM
  • 24 CPU Cores
  • 320GB SSD
  • 300Mbps Unmetered Bandwidth
  • Once per 2 Weeks Backup





    • OS: Linux / Windows 10/ Windows 11
    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

  • 128GB RAM
  • GPU: Nvidia Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • (24 Cores & 48 Threads)
  • 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 A6000

  • 256GB RAM
  • GPU: Nvidia Quadro RTX A6000
  • Dual 18-Core E5-2697v4
  • (36 cores & 72 threads)
  • 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 Dedicated Server - RTX 4090

  • 256GB RAM
  • GPU: GeForce RTX 4090
  • Dual 18-Core E5-2697v4
        (36 cores & 72 threads)
  • 240GB SSD + 2TB NVMe + 8TB  SATA
  • 100Mbps-1Gbps




    • OS: Windows / Linux
    Single GPU Specifications:

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




    • Enterprise GPU Dedicated Server - A100

  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
        (36 cores & 72 threads)
  • 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


    • Multi-GPU
    Dedicated Server - 2xA100

  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
        (36 cores & 72 threads)
  • 240GB SSD + 2TB NVMe + 8TB  SATA
  • 1Gbps




    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5     TFLOPS
  • Free NVLink Included


    • Multi-GPU
    Dedicated Server - 4xA100

  • 512GB RAM
  • GPU: 4 x Nvidia A100
  • Dual 22-Core E5-2699v4
        (44 cores & 88 threads)
  • 240GB SSD + 4TB NVMe + 16TB  SATA
  • 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)

  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
        (36 cores & 72 threads)
  • 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



    • Enterprise GPU Dedicated Server - H100

  • 256GB RAM
  • GPU: Nvidia H100
  • Dual 18-Core E5-2697v4
        (36 cores & 72 threads)
  • 240GB SSD + 2TB NVMe + 8TB  SATA
  • 100Mbps-1Gbps




    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Hopper
  • CUDA Cores: 14,592
  • Tensor Cores: 456
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 183     TFLOPS



    • 6 Core Features of vLLM Hosting

    NVIDIA GPU

    High-Performance GPU Server

    Equipped with top-level NVIDIA GPUs such as H100 and A100, it supports any AI inference.

    SSD-Based Drives

    Freely Deploy any Model

    Fully compatible with the vLLM platform, users can freely choose and deploy models, including: DeepSeek-R1, Gemma 3, Phi-4, and Llama 3.

    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 for vLLM very easily and quickly.

    99.9% Uptime Guarantee

    Data Privacy and Security

    Provide dedicated servers to avoid sharing resources with other users and ensure full control of data.

    Dedicated IP

    24/7 Technical Support

    7x24 hours online support helps users solve all problems from environment configuration to model optimization.

    24/7/365 Technical Support

    Customized Service

    Based on enterprise needs, we provide customized server configuration and technical consulting services to ensure maximum resource utilization.

    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 ✅ 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
    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

    FAQs of vLLM Hosting

    Here are some frequently asked questions (FAQs) about vLLM hosting:

    What is vLLM?
    vLLM is a high-performance inference engine optimized for running large language models (LLMs) with low latency and high throughput. It is designed for serving models efficiently on GPU servers, reducing memory usage while handling multiple concurrent requests.
    To run vLLM efficiently, you'll need: ✅ GPU: NVIDIA GPU with CUDA support (e.g., A6000, A100, H100, 4090) ✅ CUDA: Version 11.8+ ✅ GPU Memory: 16GB+ VRAM for small models, 80GB+ for large models (e.g., Llama-70B) ✅ Storage: SSD/NVMe recommended for fast model loading
    vLLM supports most Hugging Face Transformer models, including: ✅ Meta’s LLaMA (Llama 2, Llama 3) ✅ DeepSeek, Qwen, Gemma, Mistral, Phi ✅ Code models (Code Llama, StarCoder, DeepSeek-Coder) ✅ MosaicML's MPT, Falcon, GPT-J, GPT-NeoX, and more
    🚫 No, vLLM is optimized for GPU inference only. If you need CPU-based inference, use llama.cpp instead.
    ✅ Yes, vLLM supports multi-GPU inference using tensor-parallel-size.
    🚫 No, vLLM is only for inference. For fine-tuning, use PEFT (LoRA), Hugging Face Trainer, or DeepSpeed.
    ✅ 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 models ✅ Run on high-memory GPUs (A100, H100, 4090, A6000)
    🟠 Not directly. But you can load quantized models using bitsandbytes or AutoGPTQ before running them in vLLM.

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