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Sreeraj Sreenivasan
Sreeraj Sreenivasan

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Your Guide to Vibe Coding with a Local LLM

#localllm #vibecoding #ollama #devtools

No API costs. No rate limits. No privacy concerns. Just you, your machine, and a model that thinks at the speed of flow. A complete setup guide for local AI-powered coding.

No API costs. No rate limits. No privacy concerns. Just you, your machine, and a model that thinks at the speed of flow.

The Problem with Cloud AI for Coding

You're deep in a coding session. You're in the zone. Then your AI assistant hits a rate limit, lags for 4 seconds, or you suddenly remember you just pasted a proprietary database schema into a third-party API.

Cloud-based LLMs are incredible — but for vibe coding, that fluid, almost meditative state of rapid prototyping and iterative thinking, they're not always the right tool. Latency breaks flow. Rate limits kill momentum. Privacy is a legitimate concern for professional codebases.

The solution? Run the model locally. This guide sets up your machine as a fully self-contained AI coding environment, for free, forever.

01 — Choosing Your Runner: Why Ollama Wins

Your "runner" is the software that loads model weights and serves them via a local API. The three main contenders are Ollama, LM Studio, and llama.cpp.

Runner Best for Tradeoff
Ollama Integration, automation, IDE plugins Minimal GUI
LM Studio Discovering and testing models visually Heavier, less scriptable
llama.cpp Maximum performance tuning Requires more configuration

For vibe coding, Ollama wins. It exposes an OpenAI-compatible API at localhost:11434, which means every IDE plugin and chat UI that supports OpenAI can point straight at your local model — zero code changes required. It installs in one command and runs silently in the background.

02 — The Brain: Best Open-Weights Coding Models

Model choice depends on your hardware. Here's the current state-of-the-art landscape for coding:

Model Size Best for Min VRAM Speed
Qwen2.5-Coder 7B Autocomplete, quick edits 8GB ⚡ Fast
DeepSeek-Coder-V2 16B Architecture, debugging 12GB ⚖️ Balanced
Qwen2.5-Coder 32B Complex reasoning, refactoring 24GB 🧠 Deep

For most developers on 16–32GB unified memory (Apple Silicon) or a mid-range NVIDIA GPU, DeepSeek-Coder-V2 16B hits the sweet spot — fast enough for conversational flow, smart enough for non-trivial problems.

💡 Apple Silicon tip: Unified memory is a superpower here. A MacBook Pro M3 Max with 64GB can run a 32B model entirely in memory with impressive throughput. No discrete GPU needed.

03 — The Interface: Your Vibe Coding Cockpit

The model running in the background is just the engine. You need a cockpit. Here are the three layers:

Continue.dev (VS Code / JetBrains)

The best open-source AI coding assistant for local LLMs. Inline autocomplete, a chat sidebar, slash commands, and full Ollama support out of the box. This is your primary coding interface.

Open WebUI

A self-hosted, ChatGPT-like web interface that connects to Ollama. Perfect for longer architecture brainstorming sessions, explaining complex problems, or rubber-ducking system design — without leaving your local environment.

Aider (CLI)

A terminal-based AI pair programmer that edits your actual files and is commit-aware. Exceptional for bulk refactoring, large-scale changes across multiple files, and keeping a clean git history of AI-assisted edits.

Recommended combo: Ollama in the background → Continue.dev in VS Code for in-editor flow → Open WebUI in a browser tab for architecture chats.

04 — Step-by-Step Setup Checklist

Step 1 — Install Ollama

Visit ollama.com or run:

# macOS / Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows: download the installer from ollama.com
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Ollama runs as a background service on port 11434.

Step 2 — Pull your first model 

# Fast and lightweight (good starting point)
ollama pull qwen2.5-coder:7b

# Balanced power and speed (recommended for most setups)
ollama pull deepseek-coder-v2:16b

# Maximum capability (requires 24GB+ VRAM or unified memory)
ollama pull qwen2.5-coder:32b
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Step 3 — Test the model 

ollama run qwen2.5-coder:7b
# Type a prompt. If you get a response, your runner is working.
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Step 4 — Install Continue.dev in VS Code

Open VS Code → Extensions (Cmd+Shift+X) → search "Continue" → Install.

Continue will auto-detect your running Ollama instance.

Step 5 — Configure Continue

Open ~/.continue/config.json and add your model:

{
  "models": [
    {
      "title": "DeepSeek Coder",
      "provider": "ollama",
      "model": "deepseek-coder-v2:16b",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen2.5 Coder 7B",
    "provider": "ollama",
    "model": "qwen2.5-coder:7b",
    "apiBase": "http://localhost:11434"
  }
}
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Restart VS Code and hit Cmd+L (Mac) / Ctrl+L (Windows/Linux) to open the chat.

Step 6 — Install Open WebUI (optional, requires Docker) 

docker run -d \
  -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  ghcr.io/open-webui/open-webui:main
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Visit http://localhost:3000 and connect it to your Ollama instance.

Step 7 — Tune for speed 

# Maximize GPU offloading (set in your shell profile)
export OLLAMA_NUM_GPU_LAYERS=-1

# Enable flash attention for faster inference (supported hardware)
export OLLAMA_FLASH_ATTENTION=1
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On Apple Silicon, GPU offloading is automatic — no configuration needed.

Step 8 — Start vibe coding

Open a project in VS Code. Hit Cmd+L to open Continue. Ask it anything about your codebase. Feel the flow.

05 — Pro Tips for Maximum Performance

Use quantized models. A Q4_K_M quantized 14B model often runs faster than a Q8 7B model with comparable quality. You can specify the quantization level explicitly:

ollama pull qwen2.5-coder:14b-instruct-q4_K_M
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Keep context windows tight. Shorter context = faster generation. In Continue, set "contextLength": 8192 unless you genuinely need more. Feeding 128K tokens to every autocomplete request will kill your latency.

Use a dedicated model per task. A small 3B model for tab-completion, a 16B model for chat. Continue supports multiple model configs and you can switch with a keyboard shortcut — this is one of its best features.

Pre-warm your model. On first load, models take a few seconds to initialize. Send a dummy request when your machine starts up to keep the model warm in memory.

The Vibe Is Yours to Own

Once this stack is running, you have a private, unlimited, cost-free AI coding environment that runs entirely on your hardware. No subscriptions. No outages. No one reading your code.

The future of AI-assisted development isn't just in the cloud — it's sitting on your desk, ready to go offline.

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