Basics of AI, Deep Learning & Model Training

What Is AI and Why Is It Important?

Artificial Intelligence (AI) is a branch of computer science focused on creating machines or software that can perform tasks typically requiring human intelligence. These tasks include understanding and generating language, recognizing images, making decisions, and even learning from experience.

NVIDIA GPU Servers: Save Up to 80%

CLICK FOR A QUOTE NOW!

✔️ No Upfront Payment Required - Test First, Pay Later!

AI is now used in nearly every industry: from healthcare (detecting diseases), to finance (fraud detection), to transportation (self-driving cars), and of course, technology (chatbots, voice assistants, content generators). As a result, there is massive demand for high-performance computing infrastructure — particularly GPU servers that enable the training and deployment of AI models.

---

AI, Deep Learning & Model Training: How Do Machines Learn?

Machines learn by analyzing patterns in data. This process is called machine learning. Based on how the learning happens, we can classify it into different types:

Learning Type	Simple Explanation	Example Use
Supervised Learning	Learns from examples with correct answers (labeled data)	Classifying emails as spam or not spam
Unsupervised Learning	Finds patterns in unlabeled data without guidance	Grouping customers by behavior (clustering)
Self-Supervised Learning	Learns by predicting part of the data from other parts	Predicting the next word in a sentence
Reinforcement Learning (RL)	Learns by trial and error, using rewards and penalties	Training a robot to walk or an agent to play games
RLHF (Reinforcement Learning from Human Feedback)	Adds human judgment to fine-tune model behavior	Teaching a chatbot to give helpful, polite answers

Many modern AI models (like ChatGPT) combine these techniques to improve performance and alignment with human expectations.

---

AI, Deep Learning & Model Training: What Is an AI Model?

An AI model is a trained algorithm that can make decisions or predictions based on data. It's like a mathematical brain that learns how to respond to new inputs.

AI models come in various types depending on the data they process:

Model Type	Learns From	What It Does	Example Models
Language Models (LLMs)	Text	Understand, summarize, generate, and translate language	GPT, Claude, LLaMA, Mistral
Vision Models	Images	Recognize objects, detect patterns, or generate pictures	ResNet, YOLO, Stable Diffusion
Speech Models	Audio	Convert speech to text and vice versa	Whisper, Tacotron, wav2vec
Video Models	Video frames	Understand events over time or generate short clips	Sora, Swin Transformer
Multimodal Models	Mixed data types (text, image, etc.)	Handle tasks using multiple input forms	GPT-4 Vision, Gemini, CLIP

Each model type requires different training methods, datasets, and compute power — especially during training.

---

AI, Deep Learning & Model Training: How Are Models Trained?

Training an AI model involves feeding it data and adjusting its internal parameters until it becomes good at predicting or generating correct outputs. This process can take hours, days, or even weeks depending on the size of the model and the amount of data.

Here are the main stages:

Stage	What Happens	GPU Needs
Pretraining	The model learns general language, image, or audio patterns from massive raw datasets	Very high (multi-node GPU clusters, 8+ GPUs)
Fine-tuning	The pretrained model is adapted to a specific task (e.g. legal chatbot) using labeled data	Moderate (1–8 GPUs)
RLHF	Human feedback is used to rank or reward good answers, further training the model for helpfulness	High (multiple stages, often 4–8 GPUs)
Inference	The trained model is used to answer prompts or generate content in real time	Lower (1–2 GPUs, can be optimized for cost)

Training deep learning models can be done with data parallelism (splitting data across GPUs) or model parallelism (splitting the model itself). High-end servers often contain 4–8 GPUs working together.

---

AI, Deep Learning & Model Training: Key Technical Terms (In Simple Words)

Term	Meaning
Token	A small piece of text (part of a word or word) that models read and predict
Parameter	The settings inside a model that get adjusted during training — there can be billions
Epoch	One full pass through the entire training dataset
Batch Size	Number of examples processed together in one training step
Checkpoint	A saved snapshot of the model so training can resume from that point
Quantization	Compressing the model to use less memory and run faster by using simpler number types (e.g. INT8 instead of FP32)
Inference	The process of running a trained model to generate results

Understanding these concepts helps you evaluate how "big" a model is and what hardware it needs.

---

AI, Deep Learning & Model Training: What Hardware Is Needed for AI?

AI workloads require massive parallel computation, which is what GPUs are designed for. CPUs are general-purpose, but GPUs can process thousands of operations at once — ideal for training and inference.

Task	GPU Requirements	Example GPUs
Pretraining	Requires massive compute power, large memory, and high-speed interconnects	A100 80GB, H100 80GB, MI300X
Fine-tuning	Needs fewer GPUs, but still high memory and bandwidth	A100, RTX 6000 Ada, L40
Inference	Performance and memory are important, but can be optimized	A40, L40, RTX 6000 Ada
Image Generation	High VRAM and fast computation for stable diffusion models	A100, L40, V100, RTX 4090
Speech/Audio	Good memory speed and processing, not always high VRAM	A40, RTX 6000, 3090

GPU choice depends on the model size, task type, and whether the goal is to train, fine-tune, or deploy the model. Refurbished GPUs like the A100 40GB, A40, or V100 are still widely used because they offer excellent performance at a much lower cost than new models.

---

AI, Deep Learning & Model Training: What Are Real Customers Doing With AI?

Startups (LLMs, Chatbots, AI Tools)

• Use case: Build customer service bots, knowledge base assistants, internal tools

• Models: LLaMA, Mistral, GPT-J, Falcon

• Tasks: Fine-tuning or inference

• Typical setup: 2–4x A100 or RTX 6000 Ada servers

• Budget: Often tight → prefer refurbished high-end servers

---

Creative Companies (AI Images, Video Tools)

• Use case: Product photo generation, concept art, marketing visuals

• Models: Stable Diffusion, ControlNet, Runway

• Tasks: Image/video generation

• Typical setup: 1–4x L40, A100, RTX Ada, with NVMe SSDs

• Focus: High VRAM, silent operation, thermal management

  
  

---

Enterprises / SaaS Builders

• Use case: Private chatbots, document Q&A, internal GPTs

• Models: Open-source LLMs + Retrieval-Augmented Generation (RAG)

• Tasks: Inference, limited fine-tuning

• Typical setup: 1–2x A40 or L40 servers

• Important: On-premise deployment, privacy, cost-efficiency

  
  

---

Research Labs and Universities

• Use case: Academic testing, benchmarking, small-scale experiments

• Models: ResNet, Whisper, BERT, wav2vec

• Tasks: Training and evaluation

• Typical setup: 1–2x older GPUs (V100, A40, RTX 3090/4090)

• Funding: Often limited → refurbished gear preferred

  
  

---

Hosting Providers / GPU Cloud Startups

• Use case: Renting GPU power to AI developers and researchers

• Models: Any customer-chosen model (LLM, image gen, etc.)

• Tasks: All workloads: training, fine-tuning, inference

• Typical setup: 4–8x GPU rackmount servers, Infiniband, 1TB+ RAM

• Needs: Reliable power, cooling, support for multi-GPU communication

  
  

---

Voice/Audio AI Startups

• Use case: Meeting transcription, podcast search, voice synthesis

• Models: Whisper, Tacotron, MetaVoice

• Tasks: Training, inference

• Typical setup: 1–2x A40, RTX 6000 Ada

• Important: Fast CPU-GPU communication and I/O, mid-size VRAM

  
  

---

Developers / Solo Builders / Hobbyists

• Use case: Experimenting with open-source models at home or in labs

• Models: LLaMA, Mistral, BERT, SD, Whisper

• Tasks: Small-scale training, inference

• Typical setup: 1x GPU (V100, RTX 4090, A40)

• Key concerns: Noise, size, affordability, easy Linux setup

---

NVIDIA GPU Servers: Save Up to 80%

CLICK FOR A QUOTE NOW!

✔️ No Upfront Payment Required - Test First, Pay Later!