RTX 5060 Ti 16GB for Qwen Coder 7B

Qwen Coder 7B is purpose-built for code. On the RTX 5060 Ti 16GB at our hosting it fits comfortably at FP8 or AWQ with plenty of room for fill-in-middle autocomplete and code chat.

Contents

• VRAM fit
• Deployment
• Fill-in-middle
• IDE integration
• vs Qwen Coder 14B

Fit

Deployment

python -m vllm.entrypoints.openai.api_server \
  --model Qwen/Qwen2.5-Coder-7B-Instruct-AWQ \
  --quantization awq \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.92 \
  --enable-prefix-caching

32k context matters for code – large file edits and multi-file context use it.

Fill-in-Middle

Qwen Coder emits and accepts FIM special tokens for IDE autocomplete:

<|fim_prefix|>code before cursor<|fim_suffix|>code after cursor<|fim_middle|>

Continue.dev, JetBrains, and similar IDE plugins send these markers automatically. No custom parsing needed.

IDE Integration

Continue.dev config pointing at your 5060 Ti:

"models": [{
  "title": "Qwen Coder 7B",
  "provider": "openai",
  "model": "qwen-coder-7b",
  "apiBase": "https://your-server.com/v1",
  "apiKey": "sk-..."
}]

Decode speed ~95-110 t/s at AWQ – fast enough for real-time autocomplete perception.

vs 14B

For higher code quality, consider Qwen Coder 14B (still fits Blackwell 16GB at AWQ). The 14B scores ~5-10 points higher on HumanEval at ~half the speed per request.

See also: Qwen Coder 32B on larger cards, coding assistant use case.