Gemma Google Released 2025-03 chatvisionmultilingual

Gemma 3

Gemma 3 with vision support. 12B is the sweet spot for single-GPU multimodal chat.

Best for single-GPU multimodal chat
Sizes 270M · 1B · 4B · 12B · 27B
Context 128K
License Gemma License
Min VRAM (default size, Q4) 1 GB
Rec VRAM 8 GB

What is Gemma 3?

Gemma 3 is Google's open-weights language model with vision support. Released in 2025-03, it ships in 5 sizes (270M, 1B, 4B, 12B, 27B) and is licensed Gemma License. The most popular use case is single-GPU multimodal chat.

VRAM and hardware

The smallest 270M size needs at least 1 GB VRAM; the largest 27B needs around 1 GB.

Size Min VRAM (Q4_K_M) Recommended VRAM Tokens/sec on 3090
270M 1 GB 2 GB ~34 tok/s
1B 1 GB 2 GB ~34 tok/s
4B 3 GB 5 GB ~28 tok/s
12B 8 GB 12 GB ~22 tok/s
27B 17 GB 26 GB ~18 tok/s

How to run Gemma 3 locally

Option 1: Ollama (simplest)

ollama pull gemma-3
ollama run gemma-3

Option 2: Mullama (production)

mullama pull gemma-3
mullama run gemma-3

Option 3: llama.cpp (CLI)

# Download a GGUF from Hugging Face (search "gemma-3 gguf")
./llama-cli -m gemma-3.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-3.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 3

  • chat
  • vision
  • multilingual
  • edge

Hardware it fits on

  • Apple Silicon (8GB): 270M in MLX
  • Apple Silicon (16GB): 1B in MLX
  • Apple Silicon (32GB): 4B in MLX or GGUF
  • Apple Silicon (64GB+): 27B in MLX
  • RTX 3090 (24GB): 4B at Q4_K_M
  • RTX 4090 (24GB): same as 3090, ~30% faster
  • RTX 5090 (32GB): 4B at Q8_0
  • 2× RTX 6000 Ada (96GB total): 27B at Q4_K_M

Related models in the Gemma family

  • Gemma 2 — 2B, 9B, 27B · Google-quality chat at small sizes
  • Gemma 4 — e2b, e4b, 12b, 26b, 31b · frontier-class multimodal with strong reasoning

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