A comparison of three local large language model runtimes reveals that llama.cpp is the core inference engine, while Ollama and LM Studio are user-friendly wrappers built on top of it. Ollama offers a…
Local AI users are accumulating hundreds of gigabytes of model files, including multiple quantizations of the same model, image and video generation weights, and RAG datasets, often with duplicates th…
GGUF, GPTQ, and AWQ are the three dominant formats for running quantized large language models locally, each optimized for different hardware and use cases. GGUF, the format used by llama.cpp and its …
A developer has published a guide for running Google's Gemma-4 12B instruction-tuned model on Windows Subsystem for Linux 2 (WSL2) using the llama.cpp framework. The process involves installing build …
A developer benchmarked two speech-to-text models on a CPU-only Azure VM and found that using an AI agent to plan and execute the task cut costs by 62%, from $1.96 to $0.74. The agent, Neo, achieved t…
Mochallama, a new Java library, enables running llama.cpp inference directly within a Java process using JDK 22's Foreign Function and Memory (FFM) API, eliminating the need for separate daemon proces…
TensorSharp, a new open-source C# inference engine, now enables developers to run large language models locally using GGUF files. The engine supports multiple model architectures including Gemma 4, Qw…
A Reddit user reported that llama.cpp build b9455 achieved 67-81 tokens per second on a dual RTX 3090 setup running Unsloth's Qwen3.6-27B-UD-Q8_K_XL model, matching the speed of vLLM for multi-GPU inf…
A developer built Cerberus AI, a 12MB desktop application using Tauri and Rust that runs uncensored language models locally. The app auto-detects GPU VRAM, pulls appropriate model quantizations, and u…
A developer discovered that standard quantization pipelines for large language models silently discard multi-token prediction (MTP) heads, causing speculative decoding speedups to vanish despite the b…
Ollama released version 0.30.0-rc23, a pre-release that shifts the software's architecture to directly support llama.cpp instead of building on top of GGML, and adds compatibility with the GGUF file f…
The article explains how power users can download GGUF (GPT-Generated Unified Format) model files directly from Hugging Face, quantize them (using Q4_K_M as the optimal balance of size and quality), a…
This article provides a guide for building a self-hosted AI code review tool in Python using Ollama and a locally-run language model like CodeLlama or DeepSeek-Coder. The tool reads a git diff, sends …
This article compares three dominant tools for local LLM inference in 2026: Ollama, llama.cpp, and vLLM. Ollama is recommended for personal, non-technical use due to its ease of setup, while llama.cpp…
Detailed, step-by-step guide for building llama.cpp from source on a Dell Precision T5820 workstation equipped with an RTX 3090 Ti, achieving 42 tok/s with Qwen3.6-27B. The author emphasizes that the …
The GGUF file format consolidates all necessary model components—including weights, chat templates, and special tokens—into a single file, offering a more ergonomic alternative to the scattered JSON f…
A developer has released a Python script that transplants extra tensors—such as Multi-Token Prediction (MTP) layers—from one GGUF file into another, enabling the creation of mixed-quantization models.…