Este escenario está diseñado para developers who want private ai autocomplete. La RTX 4060 Ti 16 GB ofrece el equilibrio óptimo entre capacidad (16 GB de VRAM mínimos), disponibilidad en el mercado y coste relativo para los casos de uso de este escenario.

Con 16 GB de VRAM puedes cargar los modelos recomendados en cuantización Q4 sin sacrificar demasiada calidad. El software listado está seleccionado por ser open source, activamente mantenido y compatible con el hardware de este tier.

Stack de software

Ollama ↗

Inference backend and model management.

Continue.dev ↗

VS Code and JetBrains extension that connects your IDE to the local model.

VS Code ↗

Editor with native Continue.dev support.

Guía de configuración paso a paso

1
Install Ollama and pull the model: `ollama pull qwen2.5-coder:7b`.
2
Install the Continue.dev extension in VS Code from the marketplace.
3
Open Continue config (`.continue/config.json`) and add your Ollama model as a provider.
4
Press Ctrl+I in VS Code to open the code chat, or Tab for inline autocomplete.
5
For larger models (32B), run `ollama pull qwen2.5-coder:32b` and tune the context size.

Modelos compatibles recomendados

Qwen2.5-Coder 32B (Q4)

Compatibilidad con RTX 4060 Ti 16 GB → Cómo instalar Qwen2.5-Coder 32B (Q4) →

DeepSeek Coder V2 16B

Compatibilidad con RTX 4060 Ti 16 GB → Cómo instalar DeepSeek Coder V2 16B →

Qwen2.5-Coder 7B

Compatibilidad con RTX 4060 Ti 16 GB → Cómo instalar Qwen2.5-Coder 7B →

CodeLlama 7B

Compatibilidad con RTX 4060 Ti 16 GB → Cómo instalar CodeLlama 7B →

Preguntas frecuentes

What is the best coding model to run locally?

Qwen2.5-Coder 32B at Q4 is currently the best open-source coding model for local use. If you only have 8 GB of VRAM, Qwen2.5-Coder 7B offers excellent quality for its size.

Does Continue.dev work with JetBrains?

Yes, Continue.dev ships plugins for IntelliJ IDEA, PyCharm, WebStorm, and other JetBrains IDEs. Configuration is identical to VS Code.

Why 16 GB of VRAM for a coding assistant?

With 16 GB you can run DeepSeek Coder V2 (16B) at Q8 or Qwen2.5-Coder 32B at Q4 — models with significantly better code comprehension than 7B ones. For basic autocomplete, 8 GB is enough.

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Herramientas relacionadas

Planificador de presupuesto — elige GPU según tu inversión Calculadora de VRAM — comprueba si tu GPU aguanta el modelo

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