AI Stack
Artificial Intelligence is not a single technology but a layered stack of hardware, software, data, and governance systems. Each layer depends on the others, and innovation often emerges where these layers intersect.
Hardware Layer
The hardware layer is the physical foundation of AI — specialized chips, high-bandwidth memory, and dense server racks that enable massive parallel computation at scale.
| Component | Examples | Primary Role |
|---|---|---|
| Accelerators | GPUs (NVIDIA H100), TPUs, custom ASICs | Matrix math, tensor ops for training & inference |
| Memory & Interconnect | HBM3, NVLink, PCIe Gen5 | High-speed data movement, reduce bottlenecks |
| Servers & Racks | AI servers, blade racks, liquid-cooled chassis | Dense compute deployment, thermal management |
Infrastructure Layer
Above the hardware sits the infrastructure that ties everything together: compute clusters, ultra-fast networking, and advanced cooling systems that allow models to be trained and deployed reliably.
| Component | Examples | Primary Role |
|---|---|---|
| Compute Clusters | DGX SuperPOD, Cerebras CS-3 cluster | Scale training across thousands of nodes |
| Networking | InfiniBand, Ethernet 800G, CXL | Low-latency interconnect for parallel compute |
| Energy & Cooling | Direct-to-chip liquid cooling, microgrids | Keep operations stable, reduce OPEX |
Data Layer
Data is the fuel for AI systems. This layer encompasses the collection, cleaning, storage, and governance of data, ensuring it is usable, scalable, and compliant for training and inference.
| Category | Examples | Primary Role |
|---|---|---|
| Raw Data Sources | Web text, sensors, enterprise databases | Fuel for training AI models |
| Data Engineering | ETL pipelines, vector databases | Cleansing, structuring, embedding data |
| Data Governance | Anonymization, compliance tagging | Ensure quality, privacy, compliance |
Model Layer
At the core of the AI stack are the models themselves — from general-purpose foundation models to fine-tuned variants optimized for specific industries, tasks, or domains.
| Type | Examples | Primary Role |
|---|---|---|
| Foundation Models | GPT, Claude, LLaMA | General-purpose AI across domains |
| Domain-Specific Models | Med-PaLM, BloombergGPT | Specialized knowledge for verticals |
| Fine-Tuned / LoRA | Instruction-tuned, RLHF variants | Task optimization, lightweight adaption |
Middleware / Runtime Layer
The middleware layer bridges models and applications. It includes frameworks, runtimes, and orchestration tools that make training, inference, and scaling possible across diverse hardware and environments.
| Component | Examples | Primary Role |
|---|---|---|
| Frameworks | PyTorch, TensorFlow, JAX | Model training & inference APIs |
| Inference Engines | ONNX Runtime, TensorRT | Optimized execution on diverse hardware |
| Orchestration | LangChain, Ray, Kubernetes | Workflow management & scaling |
Application Layer
Applications are where AI meets the real world. This layer spans consumer apps, enterprise tools, and edge deployments such as robotics and autonomous systems.
| Domain | Examples | Primary Role |
|---|---|---|
| Consumer | Chatbots, copilots, personal assistants | Enhance productivity, user interaction |
| Enterprise | ERP copilots, AI analytics dashboards | Automate workflows, decision support |
| Industrial / Edge | Robotics, autonomous vehicles | Physical-world AI deployment |
Governance Layer
Every AI system must be guided by principles of safety, compliance, and trust. The governance layer ensures responsible use, regulatory alignment, and transparency across the entire stack.
| Focus Area | Examples | Primary Role |
|---|---|---|
| Safety & Ethics | Alignment research, fairness audits | Prevent harm, bias, misuse |
| Compliance | EU AI Act, NIST AI RMF, ISO/IEC 42001 | Meet regulatory & standards requirements |
| Trust & Transparency | Model cards, system documentation | Enable oversight, accountability |
The AI stack is only as strong as its weakest layer. Hardware provides the raw power, infrastructure makes it scalable, data ensures relevance, models drive intelligence, middleware connects the pieces, applications deliver value, and governance anchors it all in trust. Together, these seven layers form the foundation of modern AI — and understanding them holistically is essential for building systems that are powerful, resilient, and responsible.