Gemma 4
Google's frontier-tier Gemma 4 generation. Vision + tools + thinking + audio. Best single-GPU multimodal model in 2026.
Best for frontier-class multimodal with strong reasoning
Sizes e2b · e4b · 12b · 26b · 31b
Context 128K
License Gemma License
Min VRAM (default size, Q4) 4 GB
Rec VRAM 12 GB
What is Gemma 4?
Gemma 4 is Google's open-weights language model with vision support. Released in 2026-05, it ships in 5 sizes (e2b, e4b, 12b, 26b, 31b) and is licensed Gemma License. The most popular use case is frontier-class multimodal with strong reasoning.
VRAM and hardware
The smallest e2b size needs at least 4 GB VRAM; the largest 31b needs around 8 GB.
| Size | Min VRAM (Q4_K_M) | Recommended VRAM | Tokens/sec on 3090 |
|---|---|---|---|
| e2b | 4 GB | 6 GB | ~27 tok/s |
| e4b | 4 GB | 6 GB | ~27 tok/s |
| 12b | 8 GB | 12 GB | ~22 tok/s |
| 26b | 17 GB | 26 GB | ~18 tok/s |
| 31b | 20 GB | 30 GB | ~17 tok/s |
How to run Gemma 4 locally
Option 1: Ollama (simplest)
ollama pull gemma-4
ollama run gemma-4 Option 2: Mullama (production)
mullama pull gemma-4
mullama run gemma-4 Option 3: llama.cpp (CLI)
# Download a GGUF from Hugging Face (search "gemma-4 gguf")
./llama-cli -m gemma-4.Q4_K_M.gguf -p "Hello, AI!" Option 4: Python with Mullama or llama-cpp-python
from mullama import Model, Context
model = Model.load("gemma-4.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256)) What you can build with Gemma 4
- frontier
- vision
- audio
- tools
- agentic
Hardware it fits on
- Apple Silicon (8GB): e2b in MLX
- Apple Silicon (16GB): e4b in MLX
- Apple Silicon (32GB): 12b in MLX or GGUF
- Apple Silicon (64GB+): 31b in MLX
- RTX 3090 (24GB): 12b at Q4_K_M
- RTX 4090 (24GB): same as 3090, ~30% faster
- RTX 5090 (32GB): 12b at Q8_0
- 2× RTX 6000 Ada (96GB total): 31b at Q4_K_M
Related models in the Gemma family
- Gemma 2 — 2B, 9B, 27B · Google-quality chat at small sizes
- Gemma 3 — 270M, 1B, 4B, 12B, 27B · single-GPU multimodal chat
Other models you might consider
- Llama 3.1 (Meta) — 8B, general-purpose chat, agentic workflows, code
- Llama 3.2 (Meta) — 1B, low-end hardware, mobile, edge deployment
- Llama 3.2 Vision (Meta) — 11B, vision + chat on desktop GPUs
- Llama 3.3 (Meta) — 70B, best-in-class 70B for desktops
- Llama 4 (Meta) — Scout 17B (109B MoE), vision + long context (10M tokens)
- Qwen 2.5 (Alibaba) — 0.5B, strongest 7B-32B on consumer hardware