a desktop application that allows you to run large language models (LLMs) locally on your computer, without needing an internet connection or cloud services. - Home / r/LocalLLaMA / chatGPT

• LM Studio 0.4

# Performance

They can be accessed and tailored by model

Memory

• context length may have huge impact on memory consumption if going above 8k

Performance

• cpu thread can be choose on custom model preference
• as well as GPU Layer offloading

# Server

On prompt UI (green term):

• enable Server 🟢 (Status running) and configure Server settings
  • Serve on Local Network 🟠 -> allow remote access
  • Server port 1234
  • Make sure a model is loaded

Test it works

$ curl http://yves-lab:1234/v1/chat/completions \
                       -H "Content-Type: application/json" \
                       -d '{
                     "model": "qwen3",
                     "messages": [
                       {"role": "user", "content": "Hello, respond with one word"}
                     ]
                   }'

{
  "id": "chatcmpl-udbu6gq8kshmddzqmhcrwq",
  "object": "chat.completion",
  "created": 1765637850,
  "model": "qwen/qwen3-vl-8b",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello",
        "reasoning_content": "",
        "tool_calls": []
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 14,
    "completion_tokens": 2,
    "total_tokens": 16
  },
  "stats": {},
  "system_fingerprint": "qwen/qwen3-vl-8b"
}⏎ 

# Tools

Enable LLMs to interact with external functions and APIs.

# Skills

LLMs need prompts. Prompts can get very big very quickly. The so called “skills” are simply a mechanism to extend the prompt dynamically.

# Models

# Devstral 2

Second-generation Devstral for agentic coding. Built for tool use to explore codebases, edit multiple files, and power software engineering agents with newly added vision support.

see also

• The unexpected effectiveness of one-shot decompilation with Claude

# Plan then Execute

• Implement plan-then-execute strategy
• Replicating Cursor’s Agent Mode with E2B and AgentKit

# LangFuse

• Langfuse LM Studio integration - helps you trace, monitor, debug, evaluate, and analyze what your LLM-based application (or agent system) does.
  • LocalAGI vs LangFuse -

# Goose

• Goose - an open-source AI agent designed primarily to help developers by automating engineering tasks: generating code, executing commands, debugging, running workflows, integrating with tools (e.g. version control, APIs, file system), etc.

# LocalAGI

• LocalAGI / github - a self-hostable agent orchestration platform. It lets you build & run autonomous / semi-autonomous “agents” (or even teams of agents) on your own hardware (CPU or GPU), without needing cloud APIs or external services.
  • User Guide - using LocalAGI, a self-hosted AI agent platform that runs 100% locally. It covers creating and managing agents, working with agent groups, interacting with agents, monitoring agent activities, and using browser agents.

# see also

• Dolphin LLM
  • Top 10 Uncensored LLMs You Can Try Now
• What is Ollama
• Build Ghidra agent
• Heretic - Automatic censorship removal for language models
• Gemma 3 270M From Scratch
• The security paradox of local LLMs / HN