Phi-4 (14B) vs Qwen2.5 14B
Size, context window, license, approximate VRAM and the minimum local hardware each model needs — computed from our catalog and compatibility engine, not benchmarks.
| Phi-4 (14B) | Qwen2.5 14B | |
|---|---|---|
| Parameters | 14B | 14B |
| Context window | 16K tokens | 128K tokens |
| License | MIT | Apache-2.0 |
| ~VRAM @ 4-bit (Q4_K_M) | ~9 GB | ~10 GB |
| ~VRAM @ 8-bit (Q8_0) | ~15 GB | ~16 GB |
| Minimum device | NVIDIA GeForce RTX 3060 12GB | NVIDIA GeForce RTX 3060 12GB |
| Recommended device | Supermicro 8x H100 SuperServer | Supermicro 8x H100 SuperServer |
| Deployment | Local / on-prem | Local / on-prem |
| Capabilities | Reasoning, Code | Tools, Code, Reasoning, Multilingual, Long context |
Highlighted cells mark the lighter / longer / more permissive side per row, for local deployment. Informational rows have no winner.
Bottom line
Phi-4 (14B) and Qwen2.5 14B are the same size (~14B parameters), so their memory footprints are comparable. At 4-bit, Phi-4 (14B) needs about 9GB versus ~10GB, a meaningful gap when choosing a GPU. Qwen2.5 14B advertises the longer context window (128K vs 16K), which helps with long documents. Both ship under permissive licenses, easing commercial use. Both can start on a NVIDIA GeForce RTX 3060 12GB-class machine. Figures are approximate working-set estimates, not benchmarks — verify the exact release before committing hardware.
Pick Phi-4 (14B) if reasoning & analysis.
Pick Qwen2.5 14B if you need the longer 128K context window.
16GB GPU or Apple silicon at 4-bit. A current small model with strong reasoning and an MIT license.
Fits comfortably on 16GB+ cards at 4-bit; a capable everyday agent model for a small team.
Run the winner on hardware you control
Pick the model that fits your footprint, then turn the right machine into a private AI Business OS — no per-seat data leaving your premises.