phi-4 vs Qwen2.5-14B-Instruct

Side by side

VRAM is the weight size at each precision times a flat 1.2 overhead; see the methodology. The FP8 and INT4 rows need a quantized checkpoint or an engine that quantizes on load. The fit is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does. KV cache is for one sequence at 16-bit, computed from each model's config where the attention layout is known.

Which to pick

• phi-4 needs less VRAM at its published precision (32.8 GB against 33.0 GB), so it fits on a smaller GPU.
• Qwen2.5-14B-Instruct lists the longer context window (32,768 tokens against 16,384).
• Qwen2.5-14B-Instruct caches less per sequence at 32k tokens (6.0 GB against 6.3 GB), leaving more memory for batching.
• Licenses differ: phi-4 is under MIT and Qwen2.5-14B-Instruct under Apache 2.0. Read both before commercial use.

These follow only from the facts in the table above. Whether either model does your task well is a separate question this page does not answer.

Keep reading

• phi-4: full VRAM table and live GPU fit
• Qwen2.5-14B-Instruct: full VRAM table and live GPU fit
• The Phi model series
• The Qwen model series
• All models that fit in 48 GB

Other comparisons

• phi-4 vs Qwen2.5-Coder-14B-Instruct
• phi-4 vs Qwen3-14B
• phi-4 vs DeepSeek-Coder-V2-Lite-Instruct
• phi-4 vs Qwen3-14B-Base
• Qwen2.5-14B-Instruct vs DeepSeek-Coder-V2-Lite-Instruct
• Qwen2.5-14B-Instruct vs DeepSeek-V2-Lite-Chat
• Qwen2.5-14B-Instruct vs DeepSeek-V2-Lite