System Requirements for AI , ML on Servers (Full Guide)

To determine your AI system requirements on VPS, first, you should understand whether your AI model is CPU-based or GPU-based, since some AI models depend on the CPU, while others, like Generative AI, depend on the GPU.

Then, depending on your workload, you may choose your AI system hardware, including GPU, CPU, VRAM, RAM, and storage.

🤖 AI Overview:

Your budget, your AI model’s workload, its dependency on GPU or CPU, and whether it is for training or inference determine system requirements of the Linux server for AI tools.

AI System Requirements On VPS For Major AI models

There is no single universal setup for all AI models. After selecting your AI model, you should buy a server tailored for your exact needs. I will elaborate on every AI model use case, regarding the AI system requirements on VPS.

Note: Most setups below are for mid-level workloads, inference, and Zero-shot inference only that enhance inference acceleration.

1. Machine Learning (ML)

Machine Learning (excluding NLP, deep learning, and computer vision) is CPU-based and requires high clock speeds and a strong RAM.

ML runs great on VPS or dedicated servers(non-GPU), and is ideal for analytics, light inference, and structured data workloads.

Machine Learning Recommended Hardware

• CPU: Ryzen 3 3200G/Ryzen 3 2200G/Core i7-7700
• RAM: 32 GB
• Storage: 256 GB SSD NVMe SSD

Designated OperaVPS Plan: Intel Single

2. Natural Language Processing (NLP) and Large Language Model Hosting (LLM)

NLP models range from lightweight CPU-based models to large language models (LLMs) that require GPUs. In production deployments, GPU and CPU are usually combined for inference, preprocessing, and scaling.

Note: Classical NLP workloads only require a CPU, while LLM and production models are GPU-based.

To run LLM on GPU server you need:

Natural Language Processing and Large Language Model Recommended Hardware

• GPU (For LLM): AI optimized NVIDIA P4, RTX series
• VRAM: 8 GB
• CPU: 4 vCPU / 8 vCPU for LLM
• RAM: 16 GB / 32 GB For LLM
• Storage: 512 GB SSD / 1 TB SSD For LLM

Designated OperaVPS Plan: Intel Single For Classical NLP/ P4 Server For LLM

3. Deep Learning

Due to parallel computation and neural network architectures, Deep learning is heavily dependent on GPUs. Also, VRAM is needed to handle datasets. However, for model architecture design and data preprocessing, you need a decent CPU.

Deep Learning Recommended Hardware

• GPU: Newer NVIDIA RTX 30 series and higher
• VRAM: 8 GB
• CPU: Intel i7/i9 or AMD Ryzen 7/9
• RAM: 32 GB
• Storage: 1 TB SSD

Designated OperaVPS Plan: RTX 5070Ti

4. Generative Models

While mid-level hardware, described in the NLP models section, is sufficient for text-generating AI models, GPU plays a major role in image and video-generating AI models setup. Generative AI models are GPU-based, but again, you need a CPU for data collection and preprocessing, model orchestration, and manipulation.

Generative Models Recommended Hardware

• GPU: NVIDIA A Series (NVIDIA A10 – strong balance of performance and VRAM for SDXL/ NVIDIA A16 – designed for sustained workloads and multi-user environments)
• VRAM: 16 GB
• CPU: Intel i7/i9 or AMD Ryzen 7/9
• RAM: 32 GB
• Storage: 512 GB NVMe SSD

Designated OperaVPS Plan: A10 Server

5. Audio And Speech Models

AI voice models like Whisper, MusicGen, and AudioGen require a GPU to merge NLP, ML, and speech recognition to build a system that comprehends and reacts to human speech.

Audio & Speech Models Recommended Hardware

• GPU: NVIDIA A10, NVIDIA RTX series 30
• VRAM: 16 GB
• CPU: 6 cores Intel Core i7/i9 (mid level) – 16 cores AMD Ryzen 7/9 (Cost effective)
• RAM: 32 GB
• Storage: 512GB NVMe SSD

Designated OperaVPS Plan: RTX 5070Ti

6. Scientific & Research Models

The most famous AI model in the scientific field is AlphaFold. AlphaFold places considerable computational demands since it relies on multiple sequence alignment (MSA) processing and Deep Learning. Hence, it needs both a strong GPU and CPU.

Note: AlphaFold will need 700 GB to 1 TB of space to download the databases.

Scientific & Research Models (AlphaFold) Recommended Hardware

• GPU: NVIDIA A100
• VRAM: 80 GB
• CPU: 36 vCores (AMD EPYC or Intel Xeon)
• RAM: 256 GB
• Storage: 3 TB NVMe SSD

Designated OperaVPS Plan: A16 Server

System Requirements for AI Tools, ML Frameworks, and Large Models

Here is a brief self-hosting presentation about the AI tools recommended setup.

Note: These setups are for mid-level builds.

1. ComfyUI System Requirements

The recommended  image and video generation hardware of cloud GPU for ComfyUI is:

GPU: NVIDIA RTX 3060 Ti or 4060 Ti
VRAM: 12 GB
CPU: Intel i7/Ryzen 7
RAM: 32 GB
Storage: 1 TB SSD

2. InvokeAI System Requirements

GPU: NVIDIA RTX 4080, 5080
VRAM: 12 / 16 GB
CPU: Intel i7/Ryzen 7
RAM: 32 GB
Storage: 1 TB SSD

3. StableSwarmUI System Requirements

GPU: NVIDIA RTX 3080
VRAM: 12 GB
CPU: Intel i7/Ryzen 7
RAM: 32 GB
Storage: 1 TB SSD

4. Kling Video Generation Requirements

KLing AI is a cloud-based video generation AI model that cannot be self-hosted. You can either use Kling AI through the Kling AI platform, with a user account and credits, or implement its API on ComfyUI.

5. AnimateDiff System Requirements

GPU: NVIDIA RTX 4080 Ti, 4070 Ti
VRAM: 16 GB
CPU: Intel i7/i9
RAM: 32 GB
Storage: 1.5 TB SSD NVMe

6. AudioCraft (MusicGen / AudioGen) System Requirements

Audiocraft is a code base for generative audio tasks. It has two AI models, one for converting text to sound(AudioGen) and the other for text to music(MusicGen).

6.1. AudioGen System Requirements

GPU: NVIDIA RTX 4080/5070 Ti
VRAM: 16 GB
CPU: Intel i5/i7
RAM: 32 GB
Storage: 200 GB SSD NVMe

6.2. MusicGen System Requirements

GPU: NVIDIA RTX 4080/3090
VRAM: 16 GB
CPU: Intel i7/i9
RAM: 32 GB
Storage: 150 GB SSD NVMe

7. Blender Rendering System Requirements (VPS for Rendering)

GPU: NVIDIA RTX 3060/4060
VRAM: 8 GB
CPU: Intel Core i7
RAM: 32 GB
Storage: 1 TB SSD NVMe

8. AlphaFold System Requirements (Protein Folding)

GPU: NVIDIA A100 or RTX 3090
VRAM: 80 GB
CPU: 36 vCores (AMD EPYC or Intel Xeon)
RAM: 256 GB
Storage: 3 TB NVMe SSD

9. Airflow System Requirements for Workflow Automation

Note: Apache Airflow does not natively require a GPU.

CPU: Intel Core i5-6500 / Core i7-6700
RAM: 8 GB
Storage: 20 GB SSD

10. Kubeflow System Requirements for ML Pipelines

GPU(Only required for ML pipelines): NVIDIA A10
VRAM: 24 GB
CPU: Intel Core i7-4790
RAM: 32 GB
Storage: 80 GB SSD

11. Ray System Requirements (Distributed Compute)

GPU(Only for ML workloads): NVIDIA A10/ A40/ RTX 4090
VRAM: 24GB (A10/RTX 4090) – 48 GB (NVIDIA A40)
CPU: Intel Core i7-4790/Core i5-13420H
RAM: 32 GB
Storage: 1 TB NVMe SSD

12. Dask System Requirements (Parallel Processing)

CPU: Intel Core i7-4790 / Core i5-13420H
RAM: 32 GB
Storage: 100 GB NVMe SSD

13. Llama 3 System Requirements

GPU: NVIDIA GTX 1060, 1660 Super, RTX 3060/4060
CPU: AMD Ryzen 9 or Intel Core i9
RAM: 32 GB
Storage: 160 GB NVMe SSD

14. Mistral System Requirements

GPU: NVIDIA RTX 4060 Ti, 3070, 4090
CPU: Intel i7, AMD Ryzen 7/9
RAM: 32 GB
Storage: 500 GB NVMe SSD

15. Qwen System Requirements

GPU: NVIDIA RTX 3090, 4090, 5090
CPU: Intel i7, AMD Ryzen 7/9
RAM: 64 GB
Storage: 100 GB NVMe SSD

16. SDXL System Requirements (Diffusion Models hardware)

GPU: NVIDIA A Series (NVIDIA A10 – strong balance of performance and VRAM for SDXL/ NVIDIA A16 – designed for sustained workloads and multi-user environments)
CPU: Intel Core i7/i9 or AMD Ryzen 7/9 series
RAM: 32 GB
Storage: 500 GB NVMe SSD

17. Whisper Large-v3 System Requirements (Speech-to-Text)

GPU: NVIDIA RTX 3060/3070/4060 Ti
CPU: Intel i7, AMD Ryzen 7/9
RAM: 16 GB
Storage: 30 GB SSD

18. LlamaIndex System Requirements

GPU (For Local Inference): NVIDIA RTX 4080
CPU: Intel i7, AMD Ryzen 7/9
RAM: 64 GB
Storage: 1 TB SSD

Frameworks for self-hosting AI models

Frameworks are pre-built software libraries and tools that help developers build and train AI models with complex algorithms. Most of these frameworks (such as Scikit-Learn, PyTorch, TensorFlow) are written in Python, and you can view each one with its use cases in the Python Hardware Requirements article. Here is a brief list of them:

• LangChain and LlamaIndex for Python OpenAI SDK, used for request formatting, handling authentications, and parsing structured output
• FastAPI, Django, Flask, Node.js as backend frameworks
• LangChain Agents, OpenAI Assistants, and Semantic Kernel Planners as prompt orchestration tools
• Pinecone, Weaviate, and Qdrant as vector databases
• OpenAI embedding models, SentenceTransformers as embedding pipelines
• OpenAI Moderation API, Guardrails.ai as safety tools

GPU Server vs AI Server vs VPS vs Dedicated Server For AI Models

If you want to know for which server you should look, here is a brief guide:

• Go for GPU server if your model is GPU-based.
  GPU servers implement mid to high-level GPU cards and are ideal for entry to mid-level setups. You can explore GPU Servers.
• Go for AI server if your workload is heavy.
  AI servers have the mid to enterprise-level graphic cards and an ideal hardware setup for heavy workloads.
• Go for VPS if your model is CPU-based and lightweight
  A VPS is ideal for CPU-based lightweight workloads.
• Go for Dedicated server if your model is CPU-based but heavyweight.
  The Dedicated server stands in front of the VPS server, where your AI model is CPU-based but heavyweight.

Core Hardware Components for AI Server

The core components of the AI server are discussed below, but the exact hardware depends on the model’s workload and system architecture.

1. CPU

CPU is both the brain and backbone of AI servers. CPU does the data handling, orchestration, parallel pre-processing management, data transfer between system parts, and handles general system tasks. Any AI server needs a multi-core, high clock speed CPU that supports PCIe 5.0.

The 32-64 core CPUs with higher than 3 GHz clock speed are currently the standard for recommended AI server setup.

AI server Minimum CPU:
Intel Core i7/i9 and AMD Ryzen 7/9 series.

AI server Recommended CPU:

AMD EPYC, Intel Xeon, and AMD Threadripper Pro series.

2. GPU:

GPU is the prominent hardware in most AI models, both for training and inference. Thanks to parallel processing, GPU is ideal for deep learning, neural networking, and machine learning compute needs.

When talking about GPU, VRAM and VRAM allocation is also of importance. The performance of the GPU depends on the VRAM and the data transfer rate between the VRAM and the GPU.

Other factors for the AI GPU server are CUDA and Tensor Core GPU. More CUDA cores (NVIDIA) and Stream processors (AMD) mean faster AI workload processing, and Tensor cores make matrix operations faster.

For the best performance, you can get AI server.

AI server Minimum GPU:
NVIDIA GTX 1030, GTX 1050 Ti, RTX 5070 Ti, RTX 2080 Ti, RTX 3080 Ti, Nvidia P4, A2, A10, A16.

AI server Recommended GPU:

NVIDIA A100, RTX 4090, L40, or H100.

3. RAM:

RAM prominently provides fast data access for the CPU, performs OS operations, and transfers the data to the VRAM. It is necessary to remind that the system RAM should be at least twice the size of the VRAM.

AI server Minimum RAM:
32 GB is becoming the Standard, while 16 GB performs lightweight tasks well.

AI server Recommended RAM:

64 GB and 128 GB of RAM are recommended for demanding AI workloads.

4. Storage:

AI training and inference involve terabytes of data. Your AI model must be able to store that data and have fast access to it. This creates the need for a large and fast storage. The best option today for IOPS and fast read and write speed is the NVMe SSDs.

AI server Minimum Storage:
100 GB

AI server Recommended Storage:

3 TB

NVIDIA A-series GPUs Recommended Hardware (NVIDIA A10 vs A40 vs A100)

NVIDIA A2

NVIDIA A2 is an entry-level GPU that is commonly used in chatbots, edge AI inference, and small automation services. A2 is best for lightweight NLP and AI workloads and small computer vision models.

NVIDIA A2 Recommended Hardware

• CPU: 8 vCores (+3 GHz)
• System RAM: 32 GB
• Storage: 500 GB NVMe SSD

NVIDIA A10

NVIDIA A10 has the balance between cost and performance. It is primarily used for AI inference, generative AI, 3D rendering, Stable Diffusion, and virtual workstation environments.

NVIDIA A10 Recommended Hardware

• CPU: 16 vCores (+3 GHz)
• System RAM: 64 GB
• Storage: 960 GB NVMe SSD

NVIDIA A16

NVIDIA A16 is built with enterprise data centers in mind, and its most common use cases are remote workstation hosting, Virtual Desktop Infrastructure (VDI), multimedia streaming, real-time visualization, and 3D rendering.

NVIDIA A16 Recommended Hardware

• CPU: 64 vCores (+3 GHz)
• System RAM: 128 GB
• Storage: 1.5 TB NVMe SSD

NVIDIA A40

NVIDIA A40 is a data center GPU in terms of performance and multi-workload capabilities. Real-time ray tracing, Deep Learning training, simulation, heavy-weight rendering workloads, VFX rendering pipelines, multi-model AI inference, scientific simulations, and training computer vision and NLP models are the primary use cases of the NVIDIA A40.

NVIDIA A40 Recommended Hardware

• CPU: 40 vCores (+3 GHz)
• System RAM: 128 GB
• Storage: 2 TB NVMe SSD

NVIDIA A100

NVIDIA A100 is another NVIDIA data center GPU that best suits training and fine-tuning large language models (LLM), enterprise AI infrastructure, high performance computing (HPC), training large AI models, scientific research (like protein folding), and accelerating drug discovery.

NVIDIA A100 Recommended Hardware

• CPU: 64 vCores (+3 GHz)
• System RAM: 256 GB
• Storage: 3 TB NVMe SSD

VRAM vs. RAM

Simply put, RAM is where the CPU stores its temporary data, while VRAM is where the GPU stores its data.

VRAM is where the GPU stores the textures, 3D models, and image data that it works with. Since the GPU has direct, high-speed access to the VRAM, working with it enhances the speed of the AI model compared to only using the system RAM.

OperaVPS implements High-VRAM GPU Server for its users.

How To Select Your Ideal AI Server?

If you are not sure what your ideal AI server is, or cannot distinguish between an AI GPU server and an AI VPS server, you need to:

1. Training or Inference

The AI system requirements differ for training and inference models. Training an AI model demands high computational and parallel processing power, while inference models do not need computational resources that much, and low latency is a must.

2. Define your AI workload

Specify your AI workload so you can comprehend which hardware resources your project needs. You can start with the model type, data size, model complexity, and whether you are training the model or using it for inference.

3. GPU or CPU

Some AI models are CPU-based, while heavier workloads and parallel processing models require GPU instances. Defining the type of AI server in terms of GPU vs CPU will greatly help you find your ideal AI server.

4. VRAM

For most GPU-based AI models, 16 GB of VRAM is sufficient. However, we have discussed the recommended GPU VRAM requirements for each model earlier in this article.

5. Storage and RAM

AI models require extensive storage for their large databases. SSD NVMe is the best option in most AI servers. Besides, a strong RAM will make it faster to access the data.

6. Keep growth in mind

You will need to scale your AI environment up as your AI model grows. So scalability should be a prominent factor of your AI server.

FAQ