Items related to LLaMA & Ollama for Local AI Systems
Synopsis
Modern large language models are no longer confined to centralized cloud APIs. Advances in open-weight models, quantization, GPU acceleration, and local inference runtimes have made it possible to design AI systems that execute directly on developer workstations, private infrastructure, edge devices, and enterprise environments.
LLaMA and Ollama in Production AI Systems examines the architecture behind this local AI stack, connecting the foundation-model layer represented by LLaMA with the runtime and inference layer provided by Ollama. Rather than treating local AI as a collection of command-line tools, the book approaches it as a systems-engineering problem involving model execution, memory, compute, APIs, deployment, observability, security, and operational trade-offs.
The book explores how LLaMA-class transformer models are represented and executed, how Ollama manages local model execution, and how quantization and model packaging affect memory consumption, throughput, latency, and output quality. It examines the inference pipeline from tokenization and context processing through KV-cache management, sampling, streaming generation, and hardware acceleration.
It also moves beyond single-machine experimentation into application and infrastructure architecture. You will examine local inference APIs, model routing, retrieval-augmented generation, tool-using applications, agentic workflows, hybrid local-cloud architectures, and deployment patterns for environments where privacy, latency, cost, availability, or data sovereignty influence where inference should occur.
The book also addresses the operational realities that emerge when LLM inference becomes part of a production system: resource contention, model lifecycle management, observability, performance measurement, security boundaries, failure handling, capacity planning, and multi-node deployment.
What You Will Explore
LLaMA model architecture and its role in the local LLM ecosystem
Ollama's runtime and model-execution architecture
The lifecycle of a local inference request
Model packaging, configuration, and runtime parameters
GGUF and quantized model execution
Memory requirements, KV caches, context windows, and token generation
CPU, GPU, and hybrid inference trade-offs
Ollama's API layer and application integration patterns
Model routing and multi-model local inference
Retrieval-augmented generation with locally hosted models
Tool-using and agentic AI architectures
Local-first application design
Hybrid local and cloud inference architectures
Observability and performance engineering for LLM workloads
Security, isolation, and data-governance considerations
Edge, on-premises, workstation, and enterprise deployment patterns
Capacity planning and operational strategies for production systems
Who This Book Is For
This book is intended for software engineers, AI engineers, ML engineers, platform engineers, DevOps and infrastructure professionals, solution architects, and technical leaders who want to understand how local LLM systems are engineered beyond basic model execution.
It is particularly suited to readers who need to evaluate or build private AI infrastructure, integrate local inference into existing applications, design hybrid AI platforms, or understand the engineering consequences of moving LLM inference from centralized APIs to infrastructure they control.
The goal is not simply to show how to run a model locally. It is to explain how the model, runtime, hardware, application, and infrastructure layers fit together-and how those layers behave when local LLM inference becomes a production engineering concern.
"synopsis" may belong to another edition of this title.
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LLaMA & Ollama for Local AI Systems (Paperback)
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Paperback. Condition: new. Paperback. Modern large language models are no longer confined to centralized cloud APIs. Advances in open-weight models, quantization, GPU acceleration, and local inference runtimes have made it possible to design AI systems that execute directly on developer workstations, private infrastructure, edge devices, and enterprise environments.LLaMA and Ollama in Production AI Systems examines the architecture behind this local AI stack, connecting the foundation-model layer represented by LLaMA with the runtime and inference layer provided by Ollama. Rather than treating local AI as a collection of command-line tools, the book approaches it as a systems-engineering problem involving model execution, memory, compute, APIs, deployment, observability, security, and operational trade-offs.The book explores how LLaMA-class transformer models are represented and executed, how Ollama manages local model execution, and how quantization and model packaging affect memory consumption, throughput, latency, and output quality. It examines the inference pipeline from tokenization and context processing through KV-cache management, sampling, streaming generation, and hardware acceleration.It also moves beyond single-machine experimentation into application and infrastructure architecture. You will examine local inference APIs, model routing, retrieval-augmented generation, tool-using applications, agentic workflows, hybrid local-cloud architectures, and deployment patterns for environments where privacy, latency, cost, availability, or data sovereignty influence where inference should occur.The book also addresses the operational realities that emerge when LLM inference becomes part of a production system: resource contention, model lifecycle management, observability, performance measurement, security boundaries, failure handling, capacity planning, and multi-node deployment.What You Will ExploreLLaMA model architecture and its role in the local LLM ecosystemOllama's runtime and model-execution architectureThe lifecycle of a local inference requestModel packaging, configuration, and runtime parametersGGUF and quantized model executionMemory requirements, KV caches, context windows, and token generationCPU, GPU, and hybrid inference trade-offsOllama's API layer and application integration patternsModel routing and multi-model local inferenceRetrieval-augmented generation with locally hosted modelsTool-using and agentic AI architecturesLocal-first application designHybrid local and cloud inference architecturesObservability and performance engineering for LLM workloadsSecurity, isolation, and data-governance considerationsEdge, on-premises, workstation, and enterprise deployment patternsCapacity planning and operational strategies for production systemsWho This Book Is ForThis book is intended for software engineers, AI engineers, ML engineers, platform engineers, DevOps and infrastructure professionals, solution architects, and technical leaders who want to understand how local LLM systems are engineered beyond basic model execution.It is particularly suited to readers who need to evaluate or build private AI infrastructure, integrate local inference into existing applications, design hybrid AI platforms, or understand the engineering consequences of moving LLM inference from centralized APIs to infrastructure they control.The goal is not simply to show how to run a model locally. It is to explain how the model, runtime, hardware, application, and infrastructure layers fit together-and how those layers behave when local LLM inference becomes a production engineering concern. Advances in open-weight models, Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9788667492949
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LLaMA and Ollama for Local AI Systems
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LLaMA & Ollama for Local AI Systems (Paperback)
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Paperback. Condition: new. Paperback. Modern large language models are no longer confined to centralized cloud APIs. Advances in open-weight models, quantization, GPU acceleration, and local inference runtimes have made it possible to design AI systems that execute directly on developer workstations, private infrastructure, edge devices, and enterprise environments.LLaMA and Ollama in Production AI Systems examines the architecture behind this local AI stack, connecting the foundation-model layer represented by LLaMA with the runtime and inference layer provided by Ollama. Rather than treating local AI as a collection of command-line tools, the book approaches it as a systems-engineering problem involving model execution, memory, compute, APIs, deployment, observability, security, and operational trade-offs.The book explores how LLaMA-class transformer models are represented and executed, how Ollama manages local model execution, and how quantization and model packaging affect memory consumption, throughput, latency, and output quality. It examines the inference pipeline from tokenization and context processing through KV-cache management, sampling, streaming generation, and hardware acceleration.It also moves beyond single-machine experimentation into application and infrastructure architecture. You will examine local inference APIs, model routing, retrieval-augmented generation, tool-using applications, agentic workflows, hybrid local-cloud architectures, and deployment patterns for environments where privacy, latency, cost, availability, or data sovereignty influence where inference should occur.The book also addresses the operational realities that emerge when LLM inference becomes part of a production system: resource contention, model lifecycle management, observability, performance measurement, security boundaries, failure handling, capacity planning, and multi-node deployment.What You Will ExploreLLaMA model architecture and its role in the local LLM ecosystemOllama's runtime and model-execution architectureThe lifecycle of a local inference requestModel packaging, configuration, and runtime parametersGGUF and quantized model executionMemory requirements, KV caches, context windows, and token generationCPU, GPU, and hybrid inference trade-offsOllama's API layer and application integration patternsModel routing and multi-model local inferenceRetrieval-augmented generation with locally hosted modelsTool-using and agentic AI architecturesLocal-first application designHybrid local and cloud inference architecturesObservability and performance engineering for LLM workloadsSecurity, isolation, and data-governance considerationsEdge, on-premises, workstation, and enterprise deployment patternsCapacity planning and operational strategies for production systemsWho This Book Is ForThis book is intended for software engineers, AI engineers, ML engineers, platform engineers, DevOps and infrastructure professionals, solution architects, and technical leaders who want to understand how local LLM systems are engineered beyond basic model execution.It is particularly suited to readers who need to evaluate or build private AI infrastructure, integrate local inference into existing applications, design hybrid AI platforms, or understand the engineering consequences of moving LLM inference from centralized APIs to infrastructure they control.The goal is not simply to show how to run a model locally. It is to explain how the model, runtime, hardware, application, and infrastructure layers fit together-and how those layers behave when local LLM inference becomes a production engineering concern. Advances in open-w Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9788667492949
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LLaMA & Ollama for Local AI Systems (Paperback)
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Paperback. Condition: new. Paperback. Modern large language models are no longer confined to centralized cloud APIs. Advances in open-weight models, quantization, GPU acceleration, and local inference runtimes have made it possible to design AI systems that execute directly on developer workstations, private infrastructure, edge devices, and enterprise environments.LLaMA and Ollama in Production AI Systems examines the architecture behind this local AI stack, connecting the foundation-model layer represented by LLaMA with the runtime and inference layer provided by Ollama. Rather than treating local AI as a collection of command-line tools, the book approaches it as a systems-engineering problem involving model execution, memory, compute, APIs, deployment, observability, security, and operational trade-offs.The book explores how LLaMA-class transformer models are represented and executed, how Ollama manages local model execution, and how quantization and model packaging affect memory consumption, throughput, latency, and output quality. It examines the inference pipeline from tokenization and context processing through KV-cache management, sampling, streaming generation, and hardware acceleration.It also moves beyond single-machine experimentation into application and infrastructure architecture. You will examine local inference APIs, model routing, retrieval-augmented generation, tool-using applications, agentic workflows, hybrid local-cloud architectures, and deployment patterns for environments where privacy, latency, cost, availability, or data sovereignty influence where inference should occur.The book also addresses the operational realities that emerge when LLM inference becomes part of a production system: resource contention, model lifecycle management, observability, performance measurement, security boundaries, failure handling, capacity planning, and multi-node deployment.What You Will ExploreLLaMA model architecture and its role in the local LLM ecosystemOllama's runtime and model-execution architectureThe lifecycle of a local inference requestModel packaging, configuration, and runtime parametersGGUF and quantized model executionMemory requirements, KV caches, context windows, and token generationCPU, GPU, and hybrid inference trade-offsOllama's API layer and application integration patternsModel routing and multi-model local inferenceRetrieval-augmented generation with locally hosted modelsTool-using and agentic AI architecturesLocal-first application designHybrid local and cloud inference architecturesObservability and performance engineering for LLM workloadsSecurity, isolation, and data-governance considerationsEdge, on-premises, workstation, and enterprise deployment patternsCapacity planning and operational strategies for production systemsWho This Book Is ForThis book is intended for software engineers, AI engineers, ML engineers, platform engineers, DevOps and infrastructure professionals, solution architects, and technical leaders who want to understand how local LLM systems are engineered beyond basic model execution.It is particularly suited to readers who need to evaluate or build private AI infrastructure, integrate local inference into existing applications, design hybrid AI platforms, or understand the engineering consequences of moving LLM inference from centralized APIs to infrastructure they control.The goal is not simply to show how to run a model locally. It is to explain how the model, runtime, hardware, application, and infrastructure layers fit together-and how those layers behave when local LLM inference becomes a production engineering concern. Advances in open-w Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9788667492949
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Taschenbuch. Condition: Neu. LLaMA & Ollama for Local AI Systems | Devlin Nexley | Taschenbuch | Englisch | 2026 | Devlin Nexley | EAN 9788667492949 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 138073695