Phi-4 Mini 3.8B vs Gemma 2 2B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Phi-4 Mini 3.8B | Gemma 2 2B |
|---|---|---|
| Parameters | 3.8B | 2B |
| Author | Microsoft | |
| License | MIT | Gemma |
| Context window | 0k | 0k |
| VRAM at Q4 | 10 GB | 1.8 GB |
| VRAM at Q5 | 12 GB | 2.2 GB |
| VRAM at Q8 | 18 GB | 3.2 GB |
| VRAM at FP16 | 33 GB | 5 GB |
| Use cases | chat, general, small | chat, small |
Verdict
Phi-4 Mini 3.8B is significantly larger (3.8B vs 2B), so expect higher quality but heavier VRAM and slower throughput.
For unambiguous commercial use, Phi-4 Mini 3.8B has the safer license (MIT) compared to Gemma.
The two models at a glance
About Phi-4 Mini 3.8B
Microsoft's 3.8B Phi-4 Mini under MIT with native function calling, 128k context via LongRoPE, and a 200k vocab. MMLU 67.3 and HumanEval 74.4. Strengths: Native function calling at 3.8B, 128k context via LongRoPE, MIT license, 200k vocabulary improves tokenization efficiency.
About Gemma 2 2B
Google's Gemma 2 2B, a compact instruct model distilled from larger Gemmas. Small enough to run on a Raspberry Pi 5 or modest CPU. Strengths: Runs comfortably in under 2GB VRAM at Q4, Best-in-class 2B quality for its release window, Workable on commodity CPUs, Google's Gemma license permits broad use.
How they compare
Phi-4 Mini 3.8B comes from Microsoft and Gemma 2 2B from Google, they belong to the Phi and Gemma families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.
At 3.8B vs 2B parameters, Phi-4 Mini 3.8B is the larger of the two. At Q4, Gemma 2 2B fits in about 1.8 GB of VRAM versus 10 GB for the other — a 8.2 GB difference that matters on consumer GPUs.
Where they overlap on benchmarks, Phi-4 Mini 3.8B takes MMLU with 67.3 against 52.2 — a decisive 15.1-point margin. For workloads weighted toward that benchmark, Phi-4 Mini 3.8B is the stronger default.
On a typical mid-range GPU, Gemma 2 2B pushes roughly 100 tokens/sec versus 40, so it is the more responsive choice for interactive or high-volume use. For long-context work, Phi-4 Mini 3.8B offers the bigger window (125k vs 8k tokens).
Memory, quantization & throughput
Across quantization levels, Phi-4 Mini 3.8B requires Q4 ≈ 10 GB, Q5 ≈ 12 GB, Q8 ≈ 18 GB, FP16 ≈ 33 GB, while Gemma 2 2B requires Q4 ≈ 1.8 GB, Q5 ≈ 2.2 GB, Q8 ≈ 3.2 GB, FP16 ≈ 5 GB. In practice Phi-4 Mini 3.8B needs a 12 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.
Without a GPU, Phi-4 Mini 3.8B needs roughly 12 GB of system RAM to run on CPU and Gemma 2 2B about 4 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 40 tokens/sec from Phi-4 Mini 3.8B and 100 from Gemma 2 2B, scaling up to 100 and 200 tokens/sec on high-end hardware.
Which fits your GPU
Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Phi-4 Mini 3.8B or Gemma 2 2B to the card you actually own:
- On a 8 GB GPU: Phi-4 Mini 3.8B does not fit; Gemma 2 2B runs at FP16 (5 GB).
- On a 12 GB GPU: Phi-4 Mini 3.8B runs at Q5 (12 GB); Gemma 2 2B runs at FP16 (5 GB).
- On a 16 GB GPU: Phi-4 Mini 3.8B runs at Q5 (12 GB); Gemma 2 2B runs at FP16 (5 GB).
- On a 24 GB GPU: Phi-4 Mini 3.8B runs at Q8 (18 GB); Gemma 2 2B runs at FP16 (5 GB).
Benchmark scores
Reported benchmarks for Phi-4 Mini 3.8B: MMLU 67.3, HumanEval 74.4, MATH 71.5.
Reported benchmarks for Gemma 2 2B: MMLU 52.2, HellaSwag 74.9.
Bottom line: which should you pick?
- Pick Phi-4 Mini 3.8B if you need a permissive (MIT) license for commercial deployment.
- Pick Phi-4 Mini 3.8B for long-context work (up to 125k tokens).
- Pick Gemma 2 2B for lower VRAM and faster inference; pick Phi-4 Mini 3.8B for maximum headline quality.
- Pick Phi-4 Mini 3.8B if MMLU performance is your priority (67.3 vs 52.2).
- Pick Phi-4 Mini 3.8B if your workload is general.
Which GPU should you buy to run Phi-4 Mini 3.8B?
To run Phi-4 Mini 3.8B locally at Q4, you need ~10 GB of VRAM. The best value for this is a RTX 5070 (12 GB VRAM).
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Frequently asked questions
What is the difference between Phi-4 Mini 3.8B and Gemma 2 2B?
The headline differences: Phi-4 Mini 3.8B is a 3.8B model and Gemma 2 2B is 2B; their context windows differ (125k vs 8k tokens); they ship under different licenses (MIT vs Gemma). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Phi-4 Mini 3.8B and Gemma 2 2B run on a 24 GB GPU?
At a Q4 quantization, Phi-4 Mini 3.8B needs about 10 GB of VRAM and fits comfortably on a 24 GB GPU; Gemma 2 2B needs about 1.8 GB and fits comfortably on a 24 GB GPU. Gemma 2 2B is the lighter option for tight VRAM budgets.
Is Phi-4 Mini 3.8B or Gemma 2 2B more capable?
On MMLU, Phi-4 Mini 3.8B scores higher (67.3 vs 52.2), a 15.1-point advantage on this benchmark.
Which is faster, Phi-4 Mini 3.8B or Gemma 2 2B?
Gemma 2 2B is the smaller model (2B vs 3.8B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
Which license is safer for commercial use, Phi-4 Mini 3.8B or Gemma 2 2B?
Phi-4 Mini 3.8B ships under MIT, a permissive license with no usage restrictions, whereas the other is under Gemma — check its terms before commercial deployment.
Which has the longer context window, Phi-4 Mini 3.8B or Gemma 2 2B?
Phi-4 Mini 3.8B has the larger context window (125k vs 8k tokens), so it handles longer documents and codebases in a single prompt.