← Can It Run? — GPU compatibility matrix · Local LLMs guide
Qwen 2.5 14B needs 8.5 GB VRAM at Q4. The RTX 4060 8GB has 8 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 4060 8GB’s 8 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4060 8GB |
|---|---|---|
| Q4_K_M (recommended) | 8.5 GB | ❌ |
| Q8_0 (near-lossless) | 14.5 GB | ❌ |
Why it won’t fit (and what to do)
Qwen 2.5 14B needs 8.5 GB at Q4 but the RTX 4060 8GB has only 8 GB — you’re 0.5 GB short. You can offload layers to system RAM, but expect single-digit tokens/sec (painfully slow for chat).
About the RTX 4060 8GB
Architecture: Ada · VRAM: 8 GB · Price: ~$300.
Good: Cheap, efficient Ada card, DLSS 3, low power draw
Watch out: Only 8GB VRAM — caps you at 7-8B models
Check current RTX 4060 8GB price on Amazon →
Related checks
Other models on the RTX 4060 8GB
🎯 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 4060 8GB run Qwen 2.5 14B?
No — not enough VRAM without CPU offloading. Qwen 2.5 14B needs 8.5 GB VRAM at Q4_K_M; the RTX 4060 8GB has 8 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 4060 8GB get on Qwen 2.5 14B?
It won't fit in VRAM; with CPU offloading expect very slow single-digit tokens/sec.
About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.