#llama-server

7 related articles

Jul 9, 2026·4 min

Unsloth v0.1.463-beta: Fixes Studio Crash and llama-server Permission Issue

Unsloth v0.1.463-beta fixes a Studio crash caused by access-denied errors during llama-server service discovery. Improves stability for multi-user servers and Windows environments.

Jul 24, 2026·4 min

jlens-gguf: Bringing Jacobian Lens Visualization and Steering to llama.cpp Local LLMs

jlens-gguf is an open-source tool bringing Anthropic's Jacobian Lens interpretability method to GGUF and llama.cpp, enabling internal observation, real-time steering, and abliteration for both dense and MoE models.

Jul 16, 2026·2 min

llama.cpp Milestone: The Open-Source Evolution of Local LLM Inference

llama.cpp hits a new milestone, growing from a solo hobby project into core local AI inference infrastructure. Explore its iteration speed, GGUF quantization, and how AI coding agents are reshaping open-source development.

Jul 11, 2026·6 min

Why Is Qwen3 27B So Slow on a 4090? A Hands-On Guide to Local Inference Performance Optimization

RTX 4090 taking over 400 seconds to run Qwen3 27B inference? This article analyzes the core causes—VRAM overflow and CPU offloading drag—and offers targeted fixes.

Jul 8, 2026·2 min

Unsloth v0.1.461-beta: Fix for Local GGUF Vision Model Loading

Unsloth v0.1.461-beta fixes local GGUF vision model loading on llama-server in Studio, adds variant directory companion file lookup for stable multimodal deployment.

Jun 16, 2026·4 min

Llama.cpp Windows Local Deployment Guide: Run LLMs in Three Steps Without Compiling

Step-by-step guide to deploying Llama.cpp on Windows without compiling. Download pre-built packages, configure CUDA, and run GGUF quantized models locally with GPU acceleration and web UI in three simple steps.

llama.cpp MTP Acceleration Deployment Guide: Configuration Steps & Real-World Benchmarks

Tutorials

Jun 2, 2026·3 min

llama.cpp MTP Acceleration Deployment Guide: Configuration Steps & Real-World Benchmarks

Guide to enabling MTP multi-Token prediction acceleration in llama.cpp, covering CUDA setup, desktop configuration, model selection, and benchmarks showing ~60 Token/s with Qwen3 27B.