Phi-4 14B Instruct vs Gemma 3 12B Instruct
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Gemma 3 12B Instruct is more hardware-efficient — it needs 8.9 GB at Q4_K_M vs 10.3 GB for Phi-4 14B Instruct, fitting on 105 GPUs natively.
VRAM at each quantization (8k context)
| Quant | Phi-4 14B Instruct | Gemma 3 12B Instruct | Diff |
|---|---|---|---|
| FP32 | 64.2 GB | 55.8 GB | +15% |
| BF16 | 32.9 GB | 28.5 GB | +15% |
| FP16 | 32.9 GB | 28.5 GB | +15% |
| Q8_0 | 17.2 GB | 14.8 GB | +16% |
| Q6_K | 14.4 GB | 12.4 GB | +16% |
| Q5_K_M | 11.6 GB | 10.0 GB | +16% |
| Q4_K_M | 10.3 GB | 8.9 GB | +16% |
| Q3_K_M | 8.2 GB | 7.1 GB | +17% |
| Q2_K | 6.7 GB | 5.7 GB | +17% |
| NVFP4 | 9.3 GB | 8.0 GB | +17% |
Diff is Phi-4 14B Instruct relative to Gemma 3 12B Instruct. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Phi-4 14B Instruct | Gemma 3 12B Instruct |
|---|---|---|
| Org | Microsoft | |
| Parameters | 14B | 12.2B |
| Architecture | Dense | Dense |
| Context | 16k tokens | 128k tokens |
| Modalities | text | text, vision |
| License | MIT | Gemma |
| Commercial | Yes | Yes |
| Released | 2024-12-13 | 2025-03-12 |
| GPUs (native) | 99 / 107 | 105 / 107 |
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 Gemma 3 12B Instruct.
GPUs that run only Gemma 3 12B Instruct(6)
GPUs that run both natively(99)
- NVIDIA RTX 509032 GB
- NVIDIA RTX 508016 GB
- NVIDIA RTX 5070 Ti16 GB
- NVIDIA RTX 507012 GB
- NVIDIA RTX 5060 Ti 16GB16 GB
- NVIDIA RTX 50608 GB
- NVIDIA RTX 50508 GB
- NVIDIA RTX 409024 GB
- NVIDIA RTX 408016 GB
- NVIDIA RTX 4070 Ti12 GB
- NVIDIA RTX 407012 GB
- NVIDIA RTX 4060 Ti 16GB16 GB
- +87 more GPUs run both
Which should you use?
Choose Phi-4 14B Instruct if:
- • You want maximum capability and have a 11 GB+ GPU
- • Benchmark quality matters — scores 70.4 vs 60.6 on MMLU-Pro
Choose Gemma 3 12B Instruct if:
- • You have limited VRAM — it's a smaller model needing 8.9 GB vs 10.3 GB
- • Long context matters — it supports 128k tokens vs 16k
- • You need vision/image understanding
Frequently asked questions
- Which is better, Phi-4 14B Instruct or Gemma 3 12B Instruct?
- Phi-4 14B Instruct has 14B parameters vs 12.2B for Gemma 3 12B Instruct, so Phi-4 14B Instruct is the larger model. Gemma 3 12B Instruct is more hardware-efficient, needing 8.9 GB at Q4_K_M vs 10.3 GB. Gemma 3 12B Instruct runs on more GPUs natively (105 vs 99). On MMLU-Pro, Phi-4 14B Instruct scores higher (70.4 vs 60.6).
- How much VRAM does Phi-4 14B Instruct need vs Gemma 3 12B Instruct?
- At Q4_K_M quantization with 8k context, Phi-4 14B Instruct needs approximately 10.3 GB of VRAM, while Gemma 3 12B Instruct needs 8.9 GB. At FP16, Phi-4 14B Instruct requires 32.9 GB vs 28.5 GB for Gemma 3 12B Instruct.
- Can you run Phi-4 14B Instruct on the same GPUs as Gemma 3 12B Instruct?
- Yes, 99 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 5080, NVIDIA RTX 5070 Ti. However, no GPU can run Phi-4 14B Instruct without also fitting Gemma 3 12B Instruct, and 6 GPUs can run Gemma 3 12B Instruct but not Phi-4 14B Instruct.
- What is the difference between Phi-4 14B Instruct and Gemma 3 12B Instruct?
- Phi-4 14B Instruct has 14B parameters (dense) with a 16k context window. Gemma 3 12B Instruct has 12.2B parameters (dense) with a 128k context window. Licensing differs: Phi-4 14B Instruct is MIT while Gemma 3 12B Instruct is Gemma.
- Which model fits in 24 GB of VRAM, Phi-4 14B Instruct or Gemma 3 12B Instruct?
- Both fit in 24 GB of VRAM at Q4_K_M — Phi-4 14B Instruct needs 10.3 GB and Gemma 3 12B Instruct needs 8.9 GB.