Phi-4 14B Instruct vs Qwen 2.5 14B Instruct
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Phi-4 14B Instruct is more hardware-efficient — it needs 9.3 GB at Q4_K_M vs 10.0 GB for Qwen 2.5 14B Instruct, fitting on 63 GPUs natively.
VRAM at each quantization (8k context)
| Quant | Phi-4 14B Instruct | Qwen 2.5 14B Instruct | Diff |
|---|---|---|---|
| FP16 | 32.9 GB | 34.7 GB | -5% |
| Q8 | 17.2 GB | 18.3 GB | -6% |
| Q6_K | 13.3 GB | 14.2 GB | -6% |
| Q5_K_M | 11.3 GB | 12.1 GB | -7% |
| Q4_K_M | 9.3 GB | 10.0 GB | -7% |
| Q3_K_M | 7.8 GB | 8.4 GB | -7% |
| Q2_K | 6.2 GB | 6.7 GB | -8% |
Diff is Phi-4 14B Instruct relative to Qwen 2.5 14B Instruct. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Phi-4 14B Instruct | Qwen 2.5 14B Instruct |
|---|---|---|
| Org | Microsoft | Alibaba |
| Parameters | 14B | 14.7B |
| Architecture | Dense | Dense |
| Context | 16k tokens | 125k tokens |
| Modalities | text | text |
| License | MIT | Apache 2.0 |
| Commercial | Yes | Yes |
| Released | 2024-12-13 | 2024-09-19 |
| GPUs (native) | 63 / 67 | 63 / 67 |
Benchmark scores
Green = higher score (better). — = not yet available.
GPUs that run only Phi-4 14B Instruct(0)
Every GPU that runs Phi-4 14B Instruct also runs Qwen 2.5 14B Instruct.
GPUs that run only Qwen 2.5 14B Instruct(0)
Every GPU that runs Qwen 2.5 14B Instruct also runs Phi-4 14B Instruct.
GPUs that run both natively(63)
- NVIDIA RTX 509032 GB
- NVIDIA RTX 409024 GB
- NVIDIA RTX 408016 GB
- NVIDIA RTX 4070 Ti12 GB
- NVIDIA RTX 407012 GB
- NVIDIA RTX 4060 Ti 16GB16 GB
- NVIDIA RTX 40608 GB
- NVIDIA RTX 309024 GB
- NVIDIA RTX 3090 Ti24 GB
- NVIDIA RTX 3080 10GB10 GB
- NVIDIA RTX 3060 12GB12 GB
- NVIDIA H100 80GB80 GB
- +51 more GPUs run both
Which should you use?
Choose Phi-4 14B Instruct if:
- • You have limited VRAM — it's a smaller model needing 9.3 GB vs 10.0 GB
- • Benchmark quality matters — scores 56.1 vs 51.2 on MMLU-Pro
Choose Qwen 2.5 14B Instruct if:
- • You want maximum capability and have a 11 GB+ GPU
- • Long context matters — it supports 125k tokens vs 16k
Frequently asked questions
- Which is better, Phi-4 14B Instruct or Qwen 2.5 14B Instruct?
- Phi-4 14B Instruct has 14B parameters vs 14.7B for Qwen 2.5 14B Instruct, so Qwen 2.5 14B Instruct is the larger model. Phi-4 14B Instruct is more hardware-efficient, needing 9.3 GB at Q4_K_M vs 10.0 GB. On MMLU-Pro, Phi-4 14B Instruct scores higher (56.1 vs 51.2).
- How much VRAM does Phi-4 14B Instruct need vs Qwen 2.5 14B Instruct?
- At Q4_K_M quantization with 8k context, Phi-4 14B Instruct needs approximately 9.3 GB of VRAM, while Qwen 2.5 14B Instruct needs 10.0 GB. At FP16, Phi-4 14B Instruct requires 32.9 GB vs 34.7 GB for Qwen 2.5 14B Instruct.
- Can you run Phi-4 14B Instruct on the same GPUs as Qwen 2.5 14B Instruct?
- Yes, 63 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 4090, NVIDIA RTX 4080. However, no GPU can run Phi-4 14B Instruct without also fitting Qwen 2.5 14B Instruct, and no GPU can run Qwen 2.5 14B Instruct without also fitting Phi-4 14B Instruct.
- What is the difference between Phi-4 14B Instruct and Qwen 2.5 14B Instruct?
- Phi-4 14B Instruct has 14B parameters (dense) with a 16k context window. Qwen 2.5 14B Instruct has 14.7B parameters (dense) with a 125k context window. Licensing differs: Phi-4 14B Instruct is MIT while Qwen 2.5 14B Instruct is Apache 2.0.
- Which model fits in 24 GB of VRAM, Phi-4 14B Instruct or Qwen 2.5 14B Instruct?
- Both fit in 24 GB of VRAM at Q4_K_M — Phi-4 14B Instruct needs 9.3 GB and Qwen 2.5 14B Instruct needs 10.0 GB.