Understanding AI
Artificial intelligence (AI) refers to computer science processes and statistical algorithms that simulate and augment human intelligence.
Red Hat AI Enterprise
An integrated AI platform for building and running efficient, cost-effective models, agents, and applications on any infrastructure.
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What is vLLM?
vLLM is a collection of open source code that helps language models perform calculations more efficiently.
What is sovereign AI?
Sovereign AI is about owning AI technology, keeping data local, and ensuring your systems reflect your unique values and legal requirements.
Red Hat Launches Red Hat AI Enterprise to Deliver a Unified AI Platform that Spans from Metal to Agents
Red Hat introduces Red Hat AI Enterprise and Red Hat AI 3.3
Foundations of AI
What is machine learning?
Machine learning is the technique of training a computer to find patterns, make predictions, and learn from experience without being explicitly programmed.
What is deep learning?
Deep learning is an artificial intelligence (AI) technique that teaches computers to process data using an algorithm inspired by the human brain.
What are foundation models for AI?
A foundation model is a type of machine learning (ML) model that is pre trained to perform a range of tasks.
What are large language models?
A large language model (LLM) is a type of artificial intelligence that uses machine learning techniques to understand and generate human language.
SLMs vs LLMs: What are small language models?
A small language model (SLM) is a smaller version of a large language model (LLM) that has more specialized knowledge, is faster to customize, and more efficient to run.
What is AI inference?
AI inference is when an AI model provides an answer based on data. It's the final step in a complex process of machine learning technology.
AI infrastructure explained
AI infrastructure combines artificial intelligence and machine learning (AI/ML) technology to develop and deploy reliable and scalable data solutions.
What is an AI platform?
An AI platform is an integrated collection of technologies to develop, train, and run machine learning models.
Types of AI
What is generative AI?
Generative AI is a kind of artificial intelligence technology that relies on deep learning models trained on large data sets to create new content.
Predictive AI vs. generative AI
Both gen AI and predictive AI have significant differences and use cases. As AI evolves, distinguishing between these different types helps clarify their distinct capabilities.
What is agentic AI?
Agentic AI is a software system designed to interact with data and tools in a way that requires minimal human intervention.
Agentic AI vs. generative AI
Agentic AI and generative AI explained: Learn how each works, their unique strengths, and how they can collaborate for smarter solutions.
Model enhancements
What is retrieval-augmented generation?
Retrieval-augmented generation (RAG) links external resources to an LLM to enhance a generative AI model’s output accuracy.
RAG vs. fine-tuning
RAG and fine-tuning both aim to improve LLMs, but use different methods. RAG avoids altering the model, while fine-tuning requires adjusting its parameters.
What is parameter-efficient fine-tuning (PEFT)?
PEFT is a set of techniques that adjusts only a portion of parameters within an LLM to save resources.
LoRA vs. QLoRA
LoRA (Low-Rank adaptation) and QLoRA (quantized Low-Rank adaptation) are both techniques for training AI models.
What is InstructLab?
InstructLab simplifies the process of customizing large language models with private data.
What is vLLM?
vLLM is a collection of open source code that helps language models perform calculations more efficiently.
What is Model Context Protocol (MCP)?
Learn how Model Context Protocol (MCP) connects AI applications to external data sources, helping you build smarter workflows.
What is Model-as-a-Service?
Model-as-a-Service (MaaS) is an approach to delivering AI models as shared resources, allowing users within an organization to access them on demand.
What are Granite models?
IBM's Granite models are a series of LLMs created for enterprise applications. Granite models can support gen AI use cases that use language and code.
AI at scale
What is sovereign AI?
Sovereign AI is about owning AI technology, keeping data local, and ensuring your systems reflect your unique values and legal requirements.
What is llm-d?
llm-d is a Kubernetes-native, open source framework that speeds up distributed LLM inference at scale.
What is distributed inference?
Distributed inference lets AI models process workloads more efficiently by dividing the labor of inference across a group of interconnected devices.
What is enterprise AI?
Enterprise AI is the integration of artificial intelligence (AI) tools and machine learning software into large scale operations and processes. Now, businesses can solve problems in weeks rather than years.
What is edge AI?
Edge artificial intelligence (AI) is the implementation of artificial intelligence in an edge computing environment.
What is MLOps?
Machine learning operations (MLOps) is a set of workflow practices aiming to streamline the process of deploying and maintaining machine learning (ML) models.
What is LLMOps?
Large Language Model Operations (LLMOps) Large Language Model Operations (LLMOps) are operational methods used to manage large language models.
AIOps explained
AIOps (AI for IT operations) is an approach to automating IT operations with machine learning and other advanced AI techniques.
What is AI security?
AI security defends AI applications against malicious attacks that aim to weaken AI workloads, manipulate data, or steal sensitive information.
Understanding AI/ML use cases
It is important to understand the benefits and prepare for the challenges of AI/ML that are specific to your business processes and workloads.
What is AI in healthcare?
Discover the benefits and challenges of AI in healthcare and how Red Hat is helping the industry.
AI in banking
The next era of artificial intelligence (AI) and machine learning (ML) in banking will no doubt see improvements in customer service and operational efficiency.
Understanding AI in telecommunications
Learn how the right IT solutions can help your telco use AI efficiently and cost-effectively to overcome common challenges.
Red Hat AI use cases
Generative AI
Produce new content, like text and software code.
Red Hat AI lets you run the generative AI models of your choice, faster, with fewer resources, and lower inference costs.
Predictive AI
Connect patterns and forecast future outcomes.
With Red Hat AI, organizations can build, train, serve and monitor predictive models, all while maintaining consistency across the hybrid cloud.
Operationalized AI
Create systems that support the maintenance and deployment of AI at scale.
With Red Hat AI, manage and monitor the lifecycle of AI-enabled applications while saving on resources and ensuring compliance with privacy regulations.
Agentic AI
Build workflows that perform complex tasks with limited supervision.
Red Hat AI provides a flexible approach and stable foundation for building, managing and deploying agentic AI workflows within existing applications.
Red Hat AI portfolio
Scale your AI foundation
- Customize models with control.
- Optimize resource allocation.
Optimize model performance
- Fast inference at scale.
- Powered by vLLM.
Build and deploy AI applications
- Manage the full AI lifecycle.
- Implement AI guardrails.
Run LLMs on an individual server
- Develop, test, and run gen AI.
- Fast, flexible inference.
AI customer stories from Red Hat Summit and AnsibleFest 2025
Turkish Airlines doubled the speed of deployment times with organization-wide data access.
JCCM improved the region's environmental impact assessment (EIA) processes using AI.
Denizbank sped up time to market from days to minutes.
Hitachi operationalized AI across its entire business with Red Hat AI.
