Phi-4 Mini 3.8BvsGemma 2 2B
Complete comparison between Phi-4 Mini 3.8B (3.8B parameters, Microsoft) and Gemma 2 2B (2B, Google). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Phi-4 Mini 3.8B | Gemma 2 2B |
|---|---|---|
| Settings | 3.8B | 2B |
| Family | Phi | Gemma |
| Author | Microsoft | |
| Origin | US | US |
| License | MIT | Gemma |
| Context | 128,000 tokens | 8,192 tokens |
| Output | February 2025 | June 2024 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Phi-4 Mini 3.8B | Gemma 2 2B |
|---|---|---|
| Q4_K_M (lightweight) | 3 GB | 1.8 GB |
| Q5_K_M (balanced) | 3.5 GB | 2.2 GB |
| Q8 (near-lossless) | 5 GB | 3.2 GB |
| FP16 (max quality) | 8 GB | 5 GB |
| CPU-only RAM | 6 GB | 4 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Phi-4 Mini 3.8B | Gemma 2 2B |
|---|---|---|
| RTX 4090 (24 GB) | 150 tok/s · FP16 | 200 tok/s · FP16 |
| RTX 4080 (16 GB) | 65 tok/s · FP16 | 100 tok/s · FP16 |
| RTX 3060 12GB (12 GB) | 22 tok/s · FP16 | 35 tok/s · FP16 |
| Apple M4 Pro (48 GB) (36 GB) | 65 tok/s · FP16 | 100 tok/s · FP16 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
Your private, free ChatGPT on your machine in 1 hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- 30-day money-back guarantee
Strengths and weaknesses
Phi-4 Mini 3.8B
3.8B MIT with function calling. MMLU 67.3, HumanEval 74.4. 200k vocabulary, LongRoPE.
- Native function calling
- 128k ctx
- MIT License
- Anglais-first
Installation
Gemma 2 2B
Tiny and efficient. Runs on Raspberry Pi 5.
- Very lightweight (1.8 GB VRAM Q4)
- Best 2B of 2024
- Runs on a modest CPU
- Open Gemma license
- 8192 context only
- Limited on complex tasks
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Phi-4 Mini 3.8B or Gemma 2 2B?
On an RTX 4090, Phi-4 Mini 3.8B runs at FP16 (~150 tok/s), Gemma 2 2B in FP16 (~200 tok/s). In pure throughput, Gemma 2 2B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Gemma 2 2B fits in 1.8 GB versus 3 GB for its rival — a 1.2 GB difference, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.
Can these models be used in commercial production?
Phi-4 Mini 3.8B is under the MIT license — free for commercial use. Gemma 2 2B is under Gemma — also check the terms. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Gemma 2 2B (2B). More capable: Phi-4 Mini 3.8B (3.8B). If you are unsure, run the configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.