Artificial Intelligence
Explore the world of AI, machine learning, and data science.
AI Is a Tool - Your Expertise Makes It Valuable
Artificial intelligence is transforming how we work, create, and solve problems. But AI is only as useful as the person guiding it - it amplifies your existing knowledge and accelerates research, but it does not replace understanding. Whether you are exploring local AI models, building intelligent applications, or simply trying to understand the landscape, these guides will help you make informed decisions.
Wizard Tech Services offers AI & Automation services including local AI installation, model management, and training. Read our AI philosophy to understand our approach to responsible AI adoption.
Machine learning enables systems to learn patterns from data without explicit programming. Explore supervised, unsupervised, and reinforcement learning, plus feature engineering and evaluation metrics like accuracy, precision, and recall.
LLMs like GPT-5, Claude 5, Gemini, and LLaMA power conversational AI; Stable Diffusion, FLUX, and DALL-E generate images from text. Compare cloud APIs vs. local inference, parameter counts, quantization, and fine-tuning for your domain.
AI workloads are GPU-intensive - VRAM is the bottleneck. NVIDIA RTX 5090 (32 GB), H100, and B200 lead; 12+ GB cards comfortably handle 7B-13B models locally. Also covers llama.cpp CPU inference, Apple Silicon (M4 Max/Ultra), and cloud GPU rental.
Data science bridges raw data and actionable insights using statistics and programming. Python with pandas, NumPy, scikit-learn, and Jupyter notebooks is the standard toolkit. Covers cleaning, analysis, and visualization with Matplotlib and Plotly.
PyTorch and TensorFlow dominate deep learning; LangChain and LlamaIndex simplify RAG-based LLM apps; Hugging Face hosts thousands of models. Covers prompt engineering, vector databases, and deploying AI as APIs.
Computer vision enables machines to interpret images and video - object detection, facial recognition, medical imaging, and more. OpenCV handles foundational processing; YOLO and ResNet tackle complex recognition tasks.
AI Hardware
GPUs, CPUs, and cloud options for running AI models - from consumer cards to data center hardware.
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The AI Hub - Hardware & Performance section covers GPU selection, VRAM requirements, benchmarks, and AI analytics with expandable guides and real-world comparisons.
The current top consumer GPU for local AI - 32 GB GDDR7 VRAM handles 13B–34B models comfortably and runs quantized 70B models with room to spare.
Key Features:
- 32 GB GDDR7 VRAM
- Runs 13B–34B models at full precision
- Quantized 70B models fit in VRAM (4-bit)
- Blackwell tensor cores accelerate PyTorch and TensorRT
Professional-grade GPUs designed for AI training and large-scale inference - H100 80 GB is the production workhorse, Blackwell B200 (192 GB HBM3e) is the new training flagship.
Key Features:
- 80 GB (H100) / 192 GB (B200) HBM memory
- NVLink for multi-GPU scaling
- FP8/FP4 Tensor Cores for training
- Industry standard for model training
Run LLMs on CPU without a GPU using quantized GGUF models - slower but accessible on any hardware including laptops and servers.
Key Features:
- No GPU required - runs on CPU
- GGUF format with 4-bit quantization
- Apple Silicon Metal acceleration
- Powers Ollama and LM Studio backends
Apple's unified memory architecture lets M1–M4 chips run large models using shared CPU/GPU RAM - M4 Max with 128 GB handles 70B+ models.
Key Features:
- Unified memory shared between CPU and GPU
- M4 Max supports up to 128 GB unified memory
- MLX framework optimized for Apple Silicon
- Energy-efficient inference for local AI
Rent GPUs by the hour for training or inference - more affordable than hyperscaler options for short-term AI workloads.
Key Features:
- A100/H100 GPUs available on-demand
- Pay-per-hour pricing (rates vary by GPU and demand)
- Pre-built Docker templates for common frameworks
- Serverless endpoints for production inference
AMD GPUs offer a CUDA alternative via ROCm - RDNA 4 (RX 9070 XT) and the older RX 7900 XTX (24 GB) are the practical local-AI picks; PyTorch ROCm support has matured significantly.
Key Features:
- ROCm provides a CUDA-equivalent compute stack
- RX 7900 XTX still offers 24 GB VRAM
- RX 9070 XT brings RDNA 4 ML accelerators
- PyTorch + Hugging Face support is now first-class