← Can It Run? — GPU compatibility matrix · Local LLMs guide
Qwen 2.5 14B needs 8.5 GB VRAM at Q4. The RTX 4070 Ti Super 16GB has 16 GB.
🌐 Qwen 2.5 14B (Alibaba) is a 14B-parameter model for code, math, multilingual reasoning. Excellent for technical tasks and code generation. The question is whether the RTX 4070 Ti Super 16GB’s 16 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4070 Ti Super 16GB |
|---|---|---|
| Q4_K_M (recommended) | 8.5 GB | ✅ |
| Q8_0 (near-lossless) | 14.5 GB | ✅ |
Expected performance
Running Qwen 2.5 14B at Q4 on the RTX 4070 Ti Super 16GB, expect roughly ~43 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.
Headroom: 8.5 GB used of 16 GB leaves about 7.5 GB free for KV cache (context). That’s fine for 4-8K context; for 32K+ you may need a smaller batch or a lower quant.
About the RTX 4070 Ti Super 16GB
Architecture: Ada · VRAM: 16 GB · Price: ~$800.
Good: 16GB with a wide bus — fast for 14B and squeezes 27B at Q4
Watch out: Pricey for 16GB; 24GB cards are better value for big models
Check current RTX 4070 Ti Super 16GB price on Amazon →
Related checks
Other models on the RTX 4070 Ti Super 16GB
🎯 LLM Hardware Checker
Enter your exact GPU + RAM, see every model you can run.
💾 VRAM Requirements
Exact VRAM per model at Q4/Q5/Q8/FP16 with context scaling.
Frequently asked questions
Can the RTX 4070 Ti Super 16GB run Qwen 2.5 14B?
Yes — runs at Q4 and full Q8 quality. Qwen 2.5 14B needs 8.5 GB VRAM at Q4_K_M; the RTX 4070 Ti Super 16GB has 16 GB.
How much VRAM does Qwen 2.5 14B need?
Qwen 2.5 14B needs about 8.5 GB at Q4_K_M (recommended) and 14.5 GB at Q8_0.
How many tokens per second will the RTX 4070 Ti Super 16GB get on Qwen 2.5 14B?
Roughly ~43 tokens/sec at Q4 in Ollama or llama.cpp.