Phi-3.5 MinivsGemma 2 2B
Complete comparison between Phi-3.5 Mini (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-3.5 Mini | Gemma 2 2B |
|---|---|---|
| Settings | 3.8B | 2B |
| Family | Phi | Gemma |
| Author | Microsoft | |
| Origin | US | US |
| License | MIT | Gemma |
| Context | 131,072 tokens | 8,192 tokens |
| Output | August 2024 | 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-3.5 Mini | 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-3.5 Mini | 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.
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Strengths and weaknesses
Phi-3.5 Mini
Small but clever. 128k context in 4 GB.
- 128k context with 3.8B
- MIT License
- Very fast
- Surprising reasoning performance for its size
- 10 GB VRAM Q4 (overkill for 3.8B)
- Worse than Phi-4 in quality
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-3.5 Mini or Gemma 2 2B?
On an RTX 4090, Phi-3.5 Mini 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-3.5 Mini 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-3.5 Mini (3.8B). If you're 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.