Tagged "performance-tuning"
8 articles tagged performance-tuning, 29 March 2026 to 29 May 2026. Newest first.
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Tweaking Local Language Model Settings with Ollama
A practical guide to optimizing Ollama configurations for various hardware setups and use cases, helping practitioners maximize inference performance on local systems.
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One LM Studio Setting Change Makes Local LLMs Competitive With Cloud Models
A simple configuration adjustment in LM Studio dramatically improves local LLM performance, making self-hosted inference viable for production workloads previously requiring cloud APIs. This discovery highlights how software optimization can rival hardware improvements.
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One LM Studio Setting Makes Local LLMs Competitive With Cloud Models
A single configuration change in LM Studio dramatically improved local LLM performance to rival cloud-based models. This discovery highlights how optimization tuning can unlock competitive inference speeds for self-hosted deployments.
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Improving Code Quality with Local Claude and Codex Models
Technical discussion on optimizing code generation quality when running Claude and Codex models locally, covering quantization, prompt engineering, and inference parameters. Practitioners share techniques for maximizing coding task performance on consumer hardware.
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Local LLMs Work Best When You're Not Loyal to Just One
A new analysis reveals that leveraging multiple local models strategically outperforms single-model approaches for diverse inference workloads.
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Running DeepSeek R1 Locally: Your Complete Setup Guide
SitePoint publishes a comprehensive guide for setting up and running DeepSeek R1 on local hardware, covering installation, configuration, and optimization tips for self-hosted inference.
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LiteLLM Integrates with Ollama to Simplify Running 100+ Models Locally
LiteLLM now supports seamless integration with Ollama, enabling developers to run over 100 different LLMs locally without requiring code changes across different model implementations. This abstraction layer significantly reduces deployment complexity and standardizes the local inference workflow.
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Linux Significantly Outperforms Windows for Local LLM Inference
A detailed comparison shows inference running substantially faster on Linux versus Windows on identical hardware, with implications for local deployment optimization.