Local AI & Open-Source Models
Run, optimize, serve, and evaluate open models on your own hardware.
Running models locally turns model selection into a systems problem involving memory, quantization, latency, throughput, privacy, and maintenance. Use these lessons to choose a sensible deployment shape instead of chasing a benchmark number.
Running LLMs Locally
Understand the hardware, model files, runtimes, and trade-offs involved in local inference.
12 min read →Quantization Explained
Learn how lower-precision weights reduce model size and what quality you may trade away.
12 min read →LM Studio Integration
Use a desktop local-model runner as a development endpoint for AI applications.
12 min read →High-Throughput Serving
Understand batching, concurrency, and hardware choices for serving many local model requests.
12 min read →Evaluating Model Benchmarks
Read benchmark claims critically and build evaluations that reflect your actual application.
12 min read →