Gemma vs Llama
Gemma and Llama side by side: model sizes, the VRAM you need, context window, abilities, licence and which one to pick.
Side by side
| Gemma | Llama | |
|---|---|---|
| Type | Open-weight models | Open-weight models |
| Developer | Google DeepMind | Meta |
| Newest model | Gemma 4 · Mar 2026 | Llama 4 · Apr 2025 |
| Sizes | 4B · E2B · E4B · 12B · 27B · 26B · 31B | Scout 109B · Maverick 400B |
| Smallest VRAM (4-bit) | ~4.5 GB (TranslateGemma 4B) | ~74.5 GB (Llama 4 Scout 109B) |
| Context window | 256K tokens | 10M tokens |
| Abilities | Tool callingReasoning modeImage inputAudio input | Tool callingImage input |
| Licence | Apache 2.0, Gemma Terms of Use, Health AI Developer Foundations | Llama 4 Community License |
| Run it | ollama run translategemma:4b | ollama run llama4:scout |
Which one should you pick?
- Gemma runs on smaller hardware: from ~4.5 GB of GPU memory, against ~74.5 GB.
- Llama reads longer inputs: a 10M-token context window.
- Only Gemma has a reasoning mode.
- Only Gemma has audio input.
- Gemma has the simpler licence for commercial use (Apache 2.0 or MIT).
Pick Gemma if…
- you need image or audio input on small hardware
- you want Google-quality models under Apache 2.0
- you are building on-device or laptop AI
Pick Llama if…
- you need the widest tool and fine-tune support
- your stack or tutorial already targets Llama 3.1
- you want very long context on a multi-GPU server (Llama 4 Scout)
VRAM and download size
Ollama's default 4-bit builds with an 8K context. Read GB as system RAM on a CPU-only machine or a Mac.
Gemma
| Model | Download | VRAM |
|---|---|---|
| TranslateGemma 4B | 3.3 GB | ~4.5 GB |
| MedGemma 4B | 3.3 GB | ~4.5 GB |
| Gemma 4 E2B | 4.6 GB | ~6 GB |
| Gemma 4 E4B | 6.6 GB | ~8 GB |
| Gemma 4 12B | 8 GB | ~9.5 GB |
| TranslateGemma 12B | 8.1 GB | ~9.5 GB |
| TranslateGemma 27B | 17 GB | ~19.5 GB |
| MedGemma 27B | 17 GB | ~19.5 GB |
| Gemma 4 26B | 19 GB | ~21.5 GB |
| Gemma 4 31B | 20 GB | ~22.5 GB |
Llama
| Model | Download | VRAM |
|---|---|---|
| Llama 4 Scout 109B | 67 GB | ~74.5 GB |
| Llama 4 Maverick 400B | 245 GB | ~270 GB |
Frequently asked questions
Is Gemma better than Llama?
It depends on the job and your hardware. Gemma runs on smaller hardware: from ~4.5 GB of GPU memory, against ~74.5 GB. Llama reads longer inputs: a 10M-token context window. Only Gemma has a reasoning mode. For quality, test both on your own prompts: benchmark rankings change with every release.
Which needs less VRAM, Gemma or Llama?
Gemma starts at ~4.5 GB (3.3 GB download). Llama starts at ~74.5 GB (67 GB download).
Can I use Gemma and Llama commercially?
Gemma: Apache 2.0, Gemma Terms of Use, Health AI Developer Foundations. Llama: Llama 4 Community License. Check the exact licence of the model you deploy; custom licences can limit very large companies or certain uses.
More comparisons
- Qwen vs Llama
- DeepSeek vs Qwen
- gpt-oss vs Qwen
- Kimi vs DeepSeek
- GLM vs Qwen
- Mistral vs Llama
- Qwen Coder vs Devstral
- Claude vs GPT
- Claude vs Gemini
- Gemini vs GPT
- Phi vs Gemma
- Nemotron vs Qwen
- Llama 3.1 vs Llama 4
- Gemma 3 vs Gemma 4
- Qwen3 vs Qwen3.5
- Qwen3.6 vs Qwen3.8
- Gemma 4 vs Qwen3.5
- DeepSeek-R1 vs Qwen3
- gpt-oss vs Qwen3.8
- Muse Glimmer vs Qwen3.8
- Qwen3-Coder vs Devstral Small 2