8 Technical Logs Found
Complete llama.cpp tutorial for 2026. Install, compile with CUDA/Metal, run GGUF models, tune all inference flags, use the API server, speculative decoding, and benchmark your hardware.
Install Ollama 5.x on Ubuntu, macOS, and Windows. Pull and run Llama 4, Qwen3, Gemma 3, and Mistral locally. REST API setup, GPU acceleration, Open WebUI, and sovereign model management.
Speculative decoding doubles local LLM inference speed with zero quality loss. How it works, how to enable it in Ollama and llama.cpp today, and which model pairs give the best speedup.
Master GGUF quantization formats for local LLMs in 2026. Q2_K, Q4_K_M, Q5_K_S, Q8_0, F16 explained with benchmarks, VRAM tables, and exact Ollama and llama.cpp commands.
Google's Gemma 4 can now run entirely offline on mobile devices — no internet connection, no data sent to Google's servers. We explain what Gemma 4 is, how to run it locally, and why on-device AI is the biggest privacy shift in mobile computing since HTTPS.
TurboQuant eliminates KV cache memory overhead with zero accuracy loss. Complete guide: what TurboQuant is, how PolarQuant and QJL work, and how to use TurboQuant with Ollama, GGUF, and llama.cpp today — including the best current quantisation commands while TQ models are in development.
Running AI directly on your hardware — phone, PC, or wearable — is the ultimate defense against cloud data leaks. Here's what local-first AI means in 2026.
Indian developers are running Bhashini and local LLMs to avoid sending data abroad. We explore the rise of Indic AI and the shift to on-device intelligence.
