NVIDIA GPU
Rich Nvidia graphics card types, up to 48GB VRAM, powerful CUDA performance. There are also multi-card servers for you to choose from.
Ollama Hosting, Deploy Your own AI Chatbot 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. GPUMart provides a list of the best budget GPU servers for Ollama to ensure you can get the most out of this great application.
Choose Your Ollama Hosting Plans
GPUMart offers best budget GPU servers for Ollama. Cost-effective Ollama hosting is ideal to deploy your own AI Chatbot. Note: You should have at least 8 GB of VRAM (GPU Memory) available to run the 7B models, 16 GB to run the 13B models, 32 GB to run the 33B models, 64 GB to run the 70B models.
Lite GPU Dedicated Server - K620
$ 49.00/mo
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Order Now- 16GB RAM
- Quad-Core Xeon E3-1270v3
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro K620
- Microarchitecture: Maxwell
- CUDA Cores: 384
- GPU Memory: 2GB DDR3
- FP32 Performance: 0.863 TFLOPS
- Ideal for lightweight Android emulators, small LLMs, graphic processing, and more. Powerful than GPU VPS.
Flash sale to June 16
Basic GPU Dedicated Server - GTX 1660
$ 86.00/mo
45% OFF Recurring (Was $159.00)
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Order Now- 64GB RAM
- Dual 10-Core Xeon E5-2660v2
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce GTX 1660
- Microarchitecture: Turing
- CUDA Cores: 1408
- GPU Memory: 6GB GDDR6
- FP32 Performance: 5.0 TFLOPS
Flash Sale to June 16
Professional GPU VPS - A4000
$ 99.00/mo
44% OFF Recurring (Was $179.00)
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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/ Windows 11
- 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.
Half-Year Pay, Full-Year Deal
Advanced GPU Dedicated Server - A5000
$ 191.90/mo
45% OFF Recurring (Was $349.00)
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Order Now- 128GB RAM
- Dual 12-Core E5-2697v2
- 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
Advanced GPU Dedicated Server - V100
$ 229.00/mo
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Order Now- 128GB RAM
- Dual 12-Core E5-2690v3
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia V100
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
- Cost-effective for AI, deep learning, data visualization, HPC, etc
Enterprise GPU Dedicated Server - RTX 4090
$ 409.00/mo
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Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 4090
- 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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Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Enterprise GPU Dedicated Server - A100
$ 639.00/mo
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Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 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
Multi-GPU Dedicated Server - 2xA100
$ 1099.00/mo
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Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
- Free NVLink Included
- A Powerful Dual-GPU Solution for Demanding AI Workloads, Large-Scale Inference, ML Training.etc. A cost-effective alternative to A100 80GB and H100, delivering exceptional performance at a competitive price.
Multi-GPU Dedicated Server - 4xA100
$ 1899.00/mo
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Order Now- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 4 x Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
New Arrival
Enterprise GPU Dedicated Server - A100(80GB)
$ 1559.00/mo
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Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 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
Enterprise GPU Dedicated Server - H100
$ 2099.00/mo
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Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia H100
- Microarchitecture: Hopper
- CUDA Cores: 14,592
- Tensor Cores: 456
- GPU Memory: 80GB HBM2e
- FP32 Performance: 183TFLOPS
Popular LLMs and GPU Recommendations
If you're running models on the Ollama platform, selecting the right NVIDIA GPU is crucial for performance and cost-effectiveness.>>Click here for more model recommendations
DeepSeek
| Model Name | Params | Model Size | Recommended GPU cards |
| DeepSeek R1 | 7B | 4.7GB | GTX 1660 6GB or higher |
| DeepSeek R1 | 8B | 4.9GB | GTX 1660 6GB or higher |
| DeepSeek R1 | 14B | 9.0GB | RTX A4000 16GB or higher |
| DeepSeek R1 | 32B | 20GB | RTX 4090, RTX A5000 24GB, A100 40GB |
| DeepSeek R1 | 70B | 43GB | RTX A6000, A40 48GB |
| DeepSeek R1 | 671B | 404GB | Not supported yet |
| Deepseek-coder-v2 | 16B | 8.9GB | RTX A4000 16GB or higher |
| Deepseek-coder-v2 | 236B | 133GB | 2xA100 80GB, 4xA100 40GB |
Llama
| Model Name | Params | Model Size | Recommended GPU cards |
| Llama 3.3 | 70B | 43GB | A6000 48GB, A40 48GB, or higher |
| Llama 3.1 | 8B | 4.9GB | GTX 1660 6GB or higher |
| Llama 3.1 | 70B | 43GB | A6000 48GB, A40 48GB, or higher |
| Llama 3.1 | 405B | 243GB | 4xA100 80GB, or higher |
Gemma
Qwen
Phi
Mistral
6 Reasons to Choose our Ollama Hosting
GPUMart enables powerful GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.
SSD-Based Drives
You can never go wrong with our own top-notch dedicated GPU servers for Ollama, loaded with the latest Intel Xeon processors, terabytes of SSD disk space, and up to 256 GB of RAM per server.
Full Root/Admin Access
With full root/admin access, you will be able to take full control of your dedicated GPU servers for Ollama very easily and quickly.
99.9% Uptime Guarantee
With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for Ollama hosting service.
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
GPUMart provides round-the-clock technical support to help you resolve any issues related to Ollama hosting.
How to Run LLMs Locally with Ollama AI
Deploy Ollama on a bare-metal server with a dedicated or multi-GPU setup in just 10 minutes at GPU Mart. (Supports automatic deployment or manual installation.)
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Step 1
Order a GPU Server
Click Order Now, on the order page, select the pre-installed Ollama OS image for automatic setup.
Alternatively, choose a standard OS and manually install Ollama after deployment.
Alternatively, choose a standard OS and manually install Ollama after deployment.
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Step 2
Install Ollama AI
If you selected a standard OS, remotely log in to your GPU server and install the latest version of Ollama from the official website. Installation steps are the same as a local deployment.
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Step 3
Download an LLM Model
Choose and download a pre-trained LLM model compatible with Ollama. You can explore different models based on your needs:
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Step 4
Chat with the Model
Start interacting with your model directly from the terminal or via Ollama's API for integration into applications.
Key Features of Ollama
Ollama's ease of use, flexibility, and powerful LLMs make it accessible to a wide range of users.
Ease of Use
Ollama’s simple API makes it straightforward to load, run, and interact with LLMs. You can quickly get started with basic tasks without extensive coding knowledge.
Flexibility
Ollama offers a versatile platform for exploring various applications of LLMs. You can use it for text generation, language translation, creative writing, and more.
Powerful LLMs
Ollama includes pre-trained LLMs like Llama 2, renowned for its large size and capabilities. It also supports training custom LLMs tailored to your specific needs.
Community Support
Ollama actively participates in the LLM community, providing documentation, tutorials, and open-source code to facilitate collaboration and knowledge sharing.
Advantages of Ollama over ChatGPT
Ollama is an open-source platform that allows users to run large language models locally. It offers several advantages over ChatGPT
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Ollama enables users to create and customize their own models, which is not possible with ChatGPT, which is a closed product accessible only through an API provided by OpenAI.
check_circleEfficiency
Ollama is designed to be more efficient and less resource-intensive than other models, which means it requires less computational power to run. This makes it more accessible to users who may not have access to high-performance computing resources.
check_circleCost
As a self-hosted alternative to ChatGPT, Ollama is freely available, while ChatGPT may incur costs for certain versions or usage.
check_circleFlexibility
Ollama allows for running multiple models in parallel, providing customization and integration, which can be useful for tasks like autogen and other applications.
check_circleSecurity and Privacy
All components necessary for OLlama to operate, including the LLMs, are installed within your designated server. This ensures that your data remains secure and private, with no sharing or collection of information outside of your hosting environment.
check_circleSimplicity and Accessibility
Ollama is renowned for its straightforward setup process, making it accessible even to those with limited technical expertise in machine learning. This ease of use opens up opportunities for a wider range of users to experiment with and leverage LLMs.
Quick-Start Guides
Leverage our high-performance GPU servers to run Ollama at scale. Our experts have crafted guides to help you deploy, customize, and optimize Ollama for your AI workflows—whether fine-tuning models, building RAG apps, or integrating via API.
Ollama GPU Benchmarks – Model Performance
We’ve benchmarked LLMs on GPUs including P1000, T1000, GTX 1660, RTX 4060, RTX 2060, RTX 3060 Ti, A4000, V100, A5000, RTX 4090, A40, A6000, A100 40GB, Dual A100, and H100. Explore the results to select the ideal GPU server for your workload.
FAQs of Ollama Hosting
The most commonly asked questions about Ollama hosting service below.
What is Ollama?
Ollama is a platform designed to run open-source large language models (LLMs) locally on your machine. It supports a variety of models, including Llama 2, Code Llama, and others, and it bundles model weights, configuration, and data into a single package, defined by a Modelfile. Ollama is an extensible platform that enables the creation, import, and use of custom or pre-existing language models for a variety of applications.
What Nvidia GPUs are good for running Ollama?
Ollama supports Nvidia GPUs with compute capability 5.0+. Check your compute compatibility to see if your card is supported: https://developer.nvidia.com/cuda-gpus.
Examples of minimum supported cards for each series: Quadro K620/P600, Tesla P100, GeForce GTX 1650, Nvidia V100, RTX 4000.
Examples of minimum supported cards for each series: Quadro K620/P600, Tesla P100, GeForce GTX 1650, Nvidia V100, RTX 4000.
Where can I find the Ollama GitHub repository?
The Ollama GitHub repository is the hub for all things related to Ollama. You can find source code, documentation, and community discussions by searching for Ollama on GitHub or following this link (https://github.com/ollama/ollama).
How do I use the Ollama Docker image?
Using the Ollama Docker image (https://hub.docker.com/r/ollama/ollama) is a straightforward process. Once you've installed Docker, you can pull the Ollama image and run it using simple shell commands. Detailed steps can be found in Section 2 of this article.
Is Ollama compatible with Windows?
Yes, Ollama offers cross-platform support, including Windows 10 or later. You can download the Windows executable from Ollama download page (https://ollama.com/download/windows) or the GitHub repository and follow the installation instructions.
Can Ollama leverage GPU for better performance?
Yes, Ollama can utilize GPU acceleration to speed up model inference. This is particularly useful for computationally intensive tasks.
What is Ollama-UI and how does it enhance the user experience?
Ollama-UI is a graphical user interface that makes it even easier to manage your local language models. It offers a user-friendly way to run, stop, and manage models. Ollama has many good open source chat UIs, such as chatbot UI, Open WebUI, etc.
How does Ollama integrate with LangChain?
Ollama and LangChain can be used together to create powerful language model applications. LangChain provides the language models, while Ollama offers the platform to run them locally.