Model-Formats

TL;DR When you switch between models in Ollama, the service unloads the current GGUF file from memory and loads the new one from disk. Large models like llama3.1:70b or mixtral:8x7b can exceed 40GB, causing sustained disk reads that pin your SSD at maximum utilization. This becomes especially problematic when multiple users or applications trigger rapid model switches, creating a cascade of disk I/O that degrades system responsiveness. ...

TL;DR llama.cpp now handles inverse kinematics calculations through specialized GGUF models that generate joint angles and motion paths for robotic systems. You run llama-server with an IK-trained model, send it target positions as JSON prompts, and receive executable motion commands. This works entirely offline without cloud dependencies. The typical workflow involves loading a quantized IK model (Q4_K_M or Q5_K_M recommended for speed), sending coordinate targets through the OpenAI-compatible HTTP API, and parsing the structured output into robot control commands. Models like CodeLlama-IK and specialized Llama variants trained on robotics datasets handle 6-DOF arm calculations, path planning with obstacle avoidance, and real-time trajectory adjustments. ...

TL;DR This guide walks you through deploying Stable Diffusion on your own Linux machine using ComfyUI and Automatic1111 (A1111), giving you complete control over your image generation pipeline without sending prompts or outputs to third-party services. You need an NVIDIA GPU with at least 6GB VRAM for basic operation. Cards like the RTX 3060 work well for standard 512x512 images, while RTX 4090 or A6000 cards handle larger resolutions and batch processing. AMD GPUs work through ROCm but require additional configuration. CPU generation is possible but extremely slow. ...

TL;DR LM Studio provides a GUI-first approach to running Claude-style coding models locally without command-line complexity. Download the application from lmstudio.ai, install it on your Linux, macOS, or Windows system, and you gain immediate access to Hugging Face’s model repository through an integrated browser. The workflow centers on three steps: discover models through LM Studio’s search interface, download your chosen quantization format (Q4_K_M for balanced performance, Q8_0 for accuracy), and launch the built-in OpenAI-compatible API server. Models like DeepSeek Coder V2, Qwen2.5-Coder, and CodeLlama variants work particularly well for development tasks. ...

TL;DR Fine-tuning local LLMs in 2026 means adapting pre-trained models to your specific use case without cloud dependencies. Both Ollama and llama.cpp support running fine-tuned models, but the actual training happens with separate tools like Unsloth, Axolotl, or llama.cpp’s built-in training capabilities. The typical workflow: train or fine-tune using a framework that outputs GGUF format, then serve the resulting model through Ollama or llama-server. Ollama pulls base models from its library, but you can import custom GGUF files using ollama create with a Modelfile. For llama.cpp, point llama-server directly at your fine-tuned GGUF file. ...

TL;DR Tiny LLMs (1-3 billion parameters) let you run capable AI models on modest hardware without cloud dependencies. Unlike larger models requiring expensive GPUs, tiny models run smoothly on consumer laptops, Raspberry Pi 5 devices, and older workstations with 8GB RAM. This guide shows you how to deploy them locally using Ollama. ...

KoboldCpp Quick Start: Run GGUF Models with One Binary TL;DR # Download the latest release (Linux, CUDA) wget https://github.com/LostRuins/koboldcpp/releases/latest/download/koboldcpp-linux-x64-cuda1150 chmod +x koboldcpp-linux-x64-cuda1150 # Run a GGUF model ./koboldcpp-linux-x64-cuda1150 --model llama-3.1-8b-instruct.Q4_K_M.gguf \ --gpulayers 99 --contextsize 4096 --port 5001 # Web UI opens at http://localhost:5001 # KoboldAI API at http://localhost:5001/api/ # OpenAI-compatible API at http://localhost:5001/v1/chat/completions Caution: KoboldCpp binds to localhost by default. If you use --host 0.0.0.0 to allow network access, there is no built-in authentication. Restrict access with firewall rules or a reverse proxy. ...

Text Generation WebUI Setup Guide for Local LLM Inference TL;DR # Clone and run the one-click installer git clone https://github.com/oobabooga/text-generation-webui cd text-generation-webui bash start_linux.sh # Installs conda env, dependencies, launches UI # Or use Docker docker compose up -d # Access the web interface # http://localhost:7860 # Enable API mode python server.py --api --listen # API available at http://localhost:5000/v1/chat/completions Caution: The web interface has no authentication by default. Do not use --listen (which binds to 0.0.0.0) on networks you do not control. Use --listen --api-key YOUR_SECRET if exposing the API, and put a reverse proxy with auth in front for production use. ...

GGUF Quantization Explained: Choosing the Right Format for Local AI TL;DR # Check quantization of an Ollama model ollama show llama3.2:3b --modelfile | grep -i quant # Inspect a GGUF file directly python3 -c "from gguf import GGUFReader; r = GGUFReader('model.gguf'); print([kv for kv in r.fields])" # Or use llama.cpp's built-in info ./llama-quantize --help # Convert and quantize with llama.cpp ./llama-quantize input.gguf output-Q4_K_M.gguf Q4_K_M GGUF is the standard file format for running quantized LLMs locally. Quantization reduces model size and VRAM usage by representing weights with fewer bits. The tradeoff is a small reduction in output quality. Choosing the right quantization level depends on your available VRAM, the model size, and your quality requirements. ...

Ollama Model Management: Pull, Create, Copy, and Remove TL;DR # Pull a model ollama pull llama3.2:3b # List all local models ollama list # Show model details (parameters, template, license) ollama show llama3.2:3b # Copy/rename a model ollama cp llama3.2:3b my-llama # Remove a model ollama rm llama3.2:3b # Check disk usage of model storage du -sh /usr/share/ollama/.ollama/models/ Ollama stores models as layered blobs, similar to Docker images. Understanding how models are stored, tagged, and shared lets you manage disk space effectively and avoid downloading duplicate data. ...