#llamacpp

I shipped a model switcher last week. A settings screen, two local GGUF models on disk, a "Make...

What Changed GnLOLot has introduced the MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF...

How I built a mental health companion that never connects to the internet, and why the most important...

You've probably seen the benchmarks — 4x faster, 6x more mistakes, 273-vote threads on r/LocalLLaMA....

q4km vs q5km is Q4_K_M vs Q5_K_M in GGUF. Pick by VRAM headroom, quality risk, and CPU fallback. Use the free quant tool before download.

So, we left in the previous chapter with some data and insights. This time we talk about the story...

Erfahren Sie, wie llama.cpp Modelle mit dem GGUF‑Format und den Quantisierungsstufen Q4 bzw. Q8 betrieben werden, welche Performance‑Einbußen tatsächlich auftreten und wie Sie die optimale Konfiguration für maximale Geschwindigkeit und akzeptablen Genauigkeitsverlust finden.

A local LLM needs about half a gigabyte of VRAM per billion parameters at Q4, then KV cache and context stack on top. Here is how to know a model fits before you download 40 GB.

Cloud AI agents get expensive fast. This guide examines whether a Strix Halo mini PC running local models and Hermes Agent can replace recurring API costs, covering hardware, benchmarks, setup, power usage, privacy, and the workloads that make local AI financially viable.

A local LLM workflow needs more than a model prompt. It needs a verifier loop that proves the file, command, URL, or report changed before the agent claims done.

llama.cpp b9437 changes llama-bench defaults: -fa auto enables flash attention on capable hardware, -ngl to -1. Verify your old bench runs b

Run GLM 5.2 (753B) locally: 2-bit fits a 256GB Mac Studio, 4-bit wants 512GB, ~3-9 tok/s. GGUF quant picks for llama.cpp, LM Studio, and a 4090 box.