Performance
LM Studio vs llama.cpp: Why Your Model Runs Slower in the GUI
LM Studio uses llama.cpp under the hood but often runs 30-50% slower. Bundled runtime lag, UI overhead, and default settings explain the gap. How to benchmark it yourself and when the convenience is worth it.
LLM Running Slow? Two Different Problems, Two Different Fixes
Slow local LLM? Separate time-to-first-token from generation speed. Fix prompt processing with batch size and Flash Attention. Fix tok/s with GPU layers, quantization, and context length.
Why Your Local LLM Is Slow: The num_ctx VRAM Overflow Nobody Warns You About
DeepSeek-R1 14B went from 35 tok/s to 4.8 tok/s on the same GPU. The fix was one parameter. How num_ctx silently overflows VRAM and kills inference speed.
Speculative Decoding: Free 20-50% Speed Boost for Local LLMs
Speculative decoding uses a small draft model to predict tokens verified by the big model. Same output, 20-50% faster. Setup guide for LM Studio and llama.cpp.
Best New Ollama 0.17 Features: ollama launch, MLX, and OpenClaw Support
Everything new in Ollama 0.16 through 0.17.7: ollama launch for coding tools, native MLX on Apple Silicon, OpenClaw integration, web search API, and image generation. Updated March 2026.
Why Is My Local LLM So Slow? A Diagnostic Guide
Local LLM running slow? Check GPU vs CPU inference, VRAM offloading, quantization, context length, backend choice, and thermals. Find your fix in 60 seconds.
Fastest Local LLM Setup: Ollama vs vLLM vs llama.cpp Real Benchmarks
vLLM handles 4x the concurrent load of Ollama on identical hardware. But for single-user local use, Ollama is all you need. Benchmarks, memory usage, and a dead-simple decision framework. Updated for Ollama v0.17.7, vLLM v0.17.0, and llama.cpp with MCP support.