Tagged "guide"
15 articles tagged guide, 24 February 2026 to 4 September 2026. Newest first.
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Optimising On-Device Inference for Apple Silicon: Practical Guide to M-Series Deployment
Perplexity publishes comprehensive optimisation strategies for running LLMs on Apple Silicon, covering hardware-specific techniques to maximise inference performance on M-series processors.
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Good LLM Development and Usage Patterns
A practical guide outlining recommended patterns for developing and deploying LLMs in production environments, covering best practices for local and self-hosted inference.
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How to Run LLM Locally Without Falling for the Hype
Practical guide addressing common misconceptions and providing actionable steps for deploying large language models on local hardware. Emphasises realistic expectations and cost-benefit analysis.
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Local AI Isn't Just Ollama—Here's the Ecosystem That Actually Makes It Useful
A comprehensive overview of the diverse tools, frameworks, and services that comprise the modern local AI ecosystem beyond Ollama. This guide helps practitioners understand the full landscape of options available for deploying and running LLMs locally.
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Llama 4 Scout on MLX: The Complete Apple Silicon Guide (2026)
An updated guide for running Llama 4 Scout models on Apple Silicon using MLX, covering optimization techniques and practical deployment patterns for macOS-based local LLM inference.
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10GB VRAM Local LLM: The Complete Setup Guide (2026)
A comprehensive guide covering practical methods to run capable local LLMs with just 10GB of VRAM, including quantization techniques, model selection, and optimization strategies for resource-constrained systems.
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AI Licensing Marketplaces: A Guide for Publishers and Content Creators
Apex Covantage explores the emerging landscape of AI licensing marketplaces, helping publishers understand how to license content for AI model training. Important for understanding the ecosystem supporting local model development.
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Select the Right Hardware for Your Local LLM Deployment with This Online Guide
An authoritative guide for choosing appropriate hardware for local LLM inference, helping practitioners match their deployment needs to cost-effective hardware solutions.
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Pydantic-Deep: Production Deep Agents for Pydantic AI
Pydantic releases production-ready deep agent frameworks for building and deploying AI agents with structured outputs, enabling developers to run complex multi-step AI reasoning locally with type safety.
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Best Local LLM Models 2026: Developer Comparison
SitePoint's comparison guide evaluates the top LLM models available for local deployment in 2026, helping developers select the right model for their specific use cases and hardware constraints.
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How to Run Your Own Local LLM — 2026 Edition
HackerNoon publishes an updated comprehensive guide for running local LLMs, covering current best practices and tooling in 2026. The guide serves as a practical reference for practitioners setting up self-hosted inference systems.
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When Running Ollama on Your PC for Local AI, One Thing Matters More Than Most
An MSN article identifies the critical performance factor for running Ollama efficiently on personal computers. The piece highlights a key optimization principle that practitioners often overlook when deploying local LLMs.
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On-Device AI in Mobile Apps: What Should Run on the Phone vs the Cloud (A 2026 Decision Guide)
A comprehensive guide examining the trade-offs between on-device and cloud inference for mobile applications, helping developers make architectural decisions for 2026 and beyond.
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On-Device AI in Mobile Apps: What Should Run on the Phone vs the Cloud (A 2026 Decision Guide)
A comprehensive guide for developers deciding which AI workloads to run locally on mobile devices versus offload to cloud infrastructure, with practical considerations for 2026 deployment strategies.
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Enterprise Infrastructure Guide: Running Local LLMs for 70-150 Developers
A detailed discussion on designing local LLM infrastructure for agentic coding workflows across a growing development team. Covers scaling considerations, deployment architecture, and best practices for enterprise-grade on-device AI integration.