Capability · Comparison
Gemma 2 9B vs Phi-4
Gemma 2 9B and Phi-4 are both designed for on-device and edge LLM use cases. Gemma 2 is Google's 9B dense open-weights model, strong on general tasks. Phi-4 is Microsoft's 14B model trained heavily on synthetic data, engineered to excel on reasoning and math despite its size. Both are great starting points for local or self-hosted deployments.
Side-by-side
| Criterion | Gemma 2 9B | Phi-4 |
|---|---|---|
| Parameter count | 9B (dense) | 14B (dense) |
| License | Gemma Terms of Use (commercial allowed) | MIT License |
| Training data emphasis | Curated web + code | Heavy synthetic, textbook-style data |
| Reasoning benchmarks | Strong for size | Best-in-class for size (MATH, GSM8K, MMLU) |
| Context window | 8,192 tokens | 16,000 tokens |
| Multilingual | Good | English-focused |
| Inference memory (bf16) | ~18GB | ~28GB |
| Inference memory (4-bit) | ~6GB (fits on 8GB GPU) | ~9GB (fits on 12GB GPU) |
| Instruction tuning | Yes — gemma-2-9b-it | Yes — phi-4-instruct |
Verdict
Phi-4 wins on reasoning per parameter — the synthetic-data-heavy training curriculum is engineered to produce strong benchmark scores, and it shows on MATH, GSM8K, and MMLU. Gemma 2 9B is the more general-purpose pick, with a smaller memory footprint, broader multilingual support, and a license that most teams find workable. If you're building a math-tutor, code-assistant, or anywhere reasoning dominates, try Phi-4 first. If you need a well-rounded 'small LLM' default for assistants, classification, and RAG, Gemma 2 9B is the smoother pick.
When to choose each
Choose Gemma 2 9B if…
- You need a well-rounded small open-weights model.
- You need multilingual support in a small model.
- Memory is very tight (8GB VRAM target).
- You want tight integration with Google's open ecosystem (Gemma.cpp, Keras).
Choose Phi-4 if…
- Reasoning / math benchmarks are what you care about.
- You're running on a 12GB+ GPU and can spare the memory.
- Your workload is English-centric.
- MIT license is a hard requirement.
Frequently asked questions
Is Phi-4 really as strong as 70B models on reasoning?
On curated benchmarks (MATH, MMLU, GSM8K) Phi-4 is genuinely impressive for its size. On open-ended, real-world tasks, larger models still tend to win — synthetic-data training can show its seams on out-of-distribution prompts.
Can I run Gemma 2 9B on a MacBook?
Yes, especially quantised. 4-bit Gemma 2 9B runs on an M2/M3 Mac with 16GB unified memory via llama.cpp or MLX. Expect ~15-30 tokens/sec depending on the chip.
Which is better for fine-tuning?
Both are well-supported in Hugging Face TRL, Axolotl, and Unsloth. Gemma 2 has more community LoRA recipes; Phi-4 is newer but is gaining traction fast for reasoning-specific fine-tunes.
Sources
- Google — Gemma 2 model card — accessed 2026-04-20
- Microsoft — Phi-4 technical report — accessed 2026-04-20