A self-hosted coding LLM on the RTX 5060 Ti 16GB at our hosting replaces Copilot/Cursor subscriptions for small teams.
Contents
Best Coding Models (fit 16 GB)
| Model | HumanEval | Config | VRAM |
|---|---|---|---|
| Qwen 2.5 Coder 14B | 83.5 | AWQ INT4 | 9.0 GB |
| Qwen 2.5 Coder 7B | 76.8 | FP8 | 7.2 GB |
| Codestral 22B | 81.1 | AWQ INT4 + FP8 KV | 14.0 GB (tight) |
| DeepSeek-Coder-V2 Lite 16B | 81.1 | AWQ INT4 | 9.4 GB |
| StarCoder2 15B | 70.0 | AWQ INT4 | 9.5 GB |
Qwen 2.5 Coder 14B AWQ is the default – highest HumanEval at reasonable speed and strong FIM (fill-in-middle) support.
Deployment
python -m vllm.entrypoints.openai.api_server \
--model Qwen/Qwen2.5-Coder-14B-Instruct-AWQ \
--quantization awq_marlin \
--kv-cache-dtype fp8 \
--max-model-len 32768 \
--gpu-memory-utilization 0.92 \
--enable-prefix-cachingIDE Integration
- VSCode: Continue extension, point at your vLLM endpoint
- Cursor: set
OpenAI API Base URLto your server’s/v1 - JetBrains: CodeGPT plugin, custom OpenAI provider
- Neovim: llama.cpp CLI or Continue.nvim
Performance
| Workload | Latency |
|---|---|
| Inline completion (few tokens) | ~150 ms TTFT, < 300 ms total |
| “Explain this function” | ~400 ms TTFT, 3-5 s full response |
| Generate 200-line file | ~8-12 s |
| Code review (PR diff) | ~4-6 s |
Add speculative decoding with a 1B draft – 1.8-2.1x inline completion speedup.
Verdict
For a 5-10 dev team, one 5060 Ti replaces ~$100-200/month of Copilot licenses with a flat GPU fee. Privacy: your code stays on your box.
Coding Assistant on Blackwell 16GB
Qwen 2.5 Coder 14B, self-hosted. UK dedicated hosting.
Order the RTX 5060 Ti 16GBSee also: Qwen Coder 14B, Qwen Coder 7B, Codestral cost, speculative decoding, DeepSeek distill.
