LM Studio 0.4.14+ now implements Multi‑Token Prediction (MTP) speculative decoding, eliminating the need for a separate draft model and delivering roughly double the token throughput—e.g., Qwen3.6‑35B reaches about 130 tokens/s on RTX 3090—while providing a six‑step activation guide and a list of known pitfalls.
This guide shows how to configure Claude Code to run on local or cloud models within five minutes, covering hardware requirements, recommended models, step‑by‑step installation for Ollama, llama.cpp, LM Studio, and cloud‑based options, plus performance and cost comparisons.
This article explains why standard uniform quantization fails for the 228‑billion‑parameter MiniMax‑M2.7 MoE model on macOS, and compares two practical solutions—JANGTQ + MLX Studio with 2‑bit mixed‑precision achieving 91.5 % MMLU using 56.5 GB, and LM Studio + GGUF which is easier but requires at least 138 GB RAM and yields lower accuracy.
The article evaluates the GGUF‑quantized Claude‑Opus‑4.6 distilled Qwen3.5 9B model on a 16 GB Mac Mini M4 using LM Studio, detailing model sizes, performance metrics, deployment steps, API integration with Claude Code, and concluding that while the 9B version is usable, its capabilities remain limited compared to larger models.
This guide shows step‑by‑step how to enable or disable the thinking mode of Qwen3.5 series large language models across Ollama, LM Studio (GGUF and MLX), llama.cpp, and vLLM/SGLang using command‑line flags, custom model YAML files, and API parameters.
This article explores LM Studio’s evolution into a complete local AI development platform, detailing version 0.4’s architectural overhaul, headless daemon, parallel request handling, stateful REST API, UI refresh, and a suite of hidden developer features such as OpenAI‑compatible, Anthropic‑compatible APIs, CLI tools, native SDKs, and the LM Link remote‑model solution.
This guide explains how to turn off the reasoning (thinking) output of Qwen3.5 series large language models in LM Studio by creating a virtual “-no‑thinking” model directory, editing a model.yaml file, and handling common pitfalls and error messages.
This article explains the key improvements of Qwen3‑Coder, walks through two local deployment methods (LM Studio and Ollama), showcases front‑end coding examples, compares performance and hardware requirements, and offers practical recommendations for developers seeking an on‑premise AI coding assistant.
This article walks through the complete process of installing the Qwen3-30B-A3B large language model on a personal computer using LM Studio, evaluates its reasoning, creative, multilingual, and coding abilities with detailed prompts, and shares practical tips for optimizing local deployment and prompt design.
This guide shows how to select a model in LM Studio, expose a local port, start the HTTP server, and interact with it via curl commands, covering quick model listing, chat requests, and the difference between streaming and full‑response modes.
This guide shows how to set up the free, open‑source DeepSeek R1 large language model locally using LM Studio, Ollama, or Jan, choose the appropriate model size for your hardware, and integrate it into Visual Studio Code as a code‑assistant without any cost.
This guide walks you through three practical methods—using Ollama, LM Studio, and GPT4All—to install and run the open‑source Llama 3 model locally on Windows, macOS, or Ubuntu, including command‑line usage, Python integration, and prompt‑engineering techniques for formatted outputs.
This article walks through installing LM Studio on a Windows PC, downloading models from Hugging Face, using the AI Chat interface (including a Codellama‑generated Snake game), measuring resource usage, exploring the built‑in OpenAI‑compatible API, and summarizing its strengths and limitations.
