Llama-Cpp

TL;DR llama.cpp remains the fastest way to run quantized LLMs locally in 2026, but choosing the right command-line flags makes the difference between a sluggish 2 tokens/second and a responsive 30+ tokens/second experience. This guide covers the essential flags you need for optimal performance on consumer hardware. The most impactful flags control resource allocation: --n-gpu-layers offloads model layers to your GPU (start with -ngl 35 for 8GB VRAM), --threads sets CPU cores for processing (use physical cores minus 2), and --ctx-size defines context window length (2048 for chat, 8192 for document analysis). Getting these three right solves most performance issues. ...

TL;DR llama.cpp supports heterogeneous multi-GPU configurations, letting you mix NVIDIA, AMD, and even Intel Arc cards in the same system for local LLM inference. Unlike Ollama’s automatic GPU detection, llama.cpp requires explicit layer distribution using the -ngl flag combined with --split-mode and --tensor-split parameters. This gives you fine-grained control over which layers run on which card, essential when mixing a high-VRAM card with lower-capacity GPUs. ...

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 GAIA (Generative AI Integration Architecture) is an open-source framework that lets you build autonomous AI agents running entirely on your local hardware using Ollama, LM Studio, or llama.cpp as the inference backend. Unlike cloud-based agent frameworks, GAIA keeps your data on-premises and gives you full control over model selection, resource allocation, and execution policies. ...

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. ...

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. ...

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. ...

Multi-GPU Ollama Setup: Running 70B Models on Dual GPUs TL;DR A single 24GB GPU cannot run a 70B parameter LLM. The model requires approximately 40GB of VRAM at Q4 quantization. Two GPUs solve this by splitting the model across both cards. This guide covers the hardware, configuration, and performance expectations for running 70B models on dual RTX 3090s with Ollama. ...

TL;DR TurboQuant is an experimental quantization method in llama.cpp that prioritizes inference speed over traditional GGUF quantization schemes. Unlike standard Q4_K_M or Q5_K_M formats that balance compression and quality, TurboQuant applies aggressive optimization to matrix operations, reducing memory bandwidth requirements while maintaining acceptable output quality for many use cases. The key difference: TurboQuant reorganizes weight tensors for cache-friendly access patterns and uses specialized SIMD instructions that standard GGUF quantization doesn’t exploit. This means faster token generation on modern CPUs with AVX2 or AVX-512 support, though quality degradation becomes noticeable on complex reasoning tasks. ...

TL;DR Running llama.cpp in Docker containers solves the deployment complexity of local LLM inference while maintaining reproducibility across different host systems. This guide covers production-ready containerization patterns specifically for llama.cpp, focusing on aspects not typically addressed in basic setup tutorials. You’ll learn to build multi-stage Docker images that compile llama.cpp with optimal flags for your target hardware, then copy only the runtime binaries to a minimal production image. The approach reduces final image size while preserving GPU acceleration support through CUDA or ROCm layers. ...