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Local LLM tooling for developers

Guide · 2026-09-11 · 1 min read

Run models on your machine—Ollama, llama.cpp, LM Studio, vLLM, and when to pick each for coding agents and RAG.

  • local
  • ollama
  • llm
  • privacy
  • developers

Local LLM tooling for developers

Running models locally is no longer a hobby-only path. This guide maps the local stack db.fyi tracks so you can choose runtimes, UIs, and serving layers without guessing.

Start here

| Need | Start with | Hub / task | |------|------------|------------| | Chat + pull models fast | Ollama | Local AI tools | | Desktop app UX | LM Studio, GPT4All | | | Max performance on CPU/GPU | llama.cpp | AI for local LLMs | | Production-ish throughput | vLLM, TGI | AI for serving LLMs | | IDE with local models | Continue, Tabby | Best AI coding assistants |

Decision rules

  1. If you want the least friction, start with Ollama + Open WebUI / Continue.
  2. If you care about raw llama.cpp features, use llama.cpp or LM Studio (which packages it).
  3. If you are serving many concurrent users, prefer vLLM / TGI / SGLang—not a desktop chat app.
  4. If coding agents need tools, pair a local runtime with an agent that supports local endpoints (Goose, Aider, Continue).

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What we will not claim

Local does not automatically mean private or secure. Verify data paths, telemetry, and license terms on each product page.

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