RTX 5070 for Coding AI: Local Copilot with Qwen 2.5 Coder on 12 GB GDDR7

Best Coding Models for 12 GB

Recommended combo for 12 GB: Qwen 2.5 Coder 7B Q4 for chat (~4.4 GB) + Qwen 2.5 Coder 1.5B for inline autocomplete (~1 GB) = ~5.4 GB total, or Qwen 2.5 Coder 14B Q4 for chat only (~8.7 GB). The 14B model on benchmarks is close to GPT-4-level coding on HumanEval.

Continue.dev Setup

Continue.dev is the leading open-source Copilot replacement — available as a VS Code extension and JetBrains plugin.

1. Install Continue from the VS Code marketplace or continue.dev
2. Start Ollama with your coding model (see below)
3. Configure Continue to use Ollama: Cmd/Ctrl+Shift+P → "Continue: Open config"

Sample ~/.continue/config.json:

{
  "models": [
    {
      "title": "Qwen 2.5 Coder 14B",
      "provider": "ollama",
      "model": "qwen2.5-coder:14b",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen 2.5 Coder 1.5B (FIM)",
    "provider": "ollama",
    "model": "qwen2.5-coder:1.5b",
    "apiBase": "http://localhost:11434"
  },
  "embeddingsProvider": {
    "provider": "ollama",
    "model": "nomic-embed-text"
  }
}

Ollama + Continue Setup

# Pull the coding models
ollama pull qwen2.5-coder:14b    # ~8.7 GB download + storage
ollama pull qwen2.5-coder:1.5b  # ~1 GB
ollama pull nomic-embed-text     # ~0.5 GB (for codebase indexing)

# Start Ollama with persistent model loading
OLLAMA_KEEP_ALIVE=-1 ollama serve

# Verify
curl http://localhost:11434/api/tags

With OLLAMA_KEEP_ALIVE=-1, the 14B model stays resident in VRAM — no reload latency when you switch between the chat panel and inline autocomplete.

Autocomplete Latency

For inline autocomplete, first-token latency matters most — you need the suggestion to appear before the user continues typing (~100–200ms is the threshold for it feeling “live”).

The recommended split: use the 1.5B model for inline autocomplete (FIM) and the 14B model for the Continue chat panel. This gives instant tab-completions and high-quality chat/refactoring assistance from the same GPU.

Local vs GitHub Copilot

For teams with privacy requirements, the local setup wins decisively. For individual developers who don’t already have a GPU server, Copilot at £10/mo is cheaper. If you’re already using the RTX 5070 for other AI workloads, the coding assistant is essentially free.

Fill-in-the-Middle (FIM)

Qwen 2.5 Coder supports FIM tokens natively for inline autocomplete:

import ollama

def fim_complete(prefix: str, suffix: str, model="qwen2.5-coder:1.5b") -> str:
    """Fill in the middle between prefix and suffix."""
    prompt = f"<|fim_prefix|>{prefix}<|fim_suffix|>{suffix}<|fim_middle|>"
    response = ollama.generate(
        model=model,
        prompt=prompt,
        options={"temperature": 0.1, "max_tokens": 100, "stop": ["<|endoftext|>"]}
    )
    return response["response"]

# Example
prefix = "def calculate_factorial(n):\n    if n == 0:\n        return 1\n    "
suffix = "\n\nresult = calculate_factorial(5)"
completion = fim_complete(prefix, suffix)
print(completion)  # "return n * calculate_factorial(n - 1)"