Gemma 3 12B
By Google · United States
Updated 2026-07-13
Overview
The 12B sweet spot of Google's Gemma 3 line — multimodal, 128K context, and 140 languages. Fits on a single consumer GPU with room for batching.
When to pick this model
- You want strong multimodal performance on a 16GB or 24GB GPU
- You need long-context summarization or document Q&A with vision
- You're shipping a product covering many languages
- You want one general-purpose Gemma without going to 27B
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 7 GB |
| Q5_K_M | 9 GB |
| Q8_0 | 13 GB |
| FP16 (no quantization) | 24 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Gemma 3 12B fits an 8 GB consumer card at Q4_K_M (7 GB). Stepping up to Q8_0 nearly doubles the footprint to 13 GB, and unquantized FP16 weights take 24 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Gemma 3 12B needs roughly 14 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 7 tokens/sec on entry-level GPUs, on the order of 22 tokens/sec on a mid-range card, and up to 60 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Gemma 3 12B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Gemma 3 12B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q4_K_M (7 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q5_K_M (9 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (13 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (24 GB used) |
| 32 GB | RTX 5090 | FP16 (24 GB used) |
Which GPU should you buy to run Gemma 3 12B?
To run Gemma 3 12B locally at Q4, you need ~7 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Sweet spot for multimodal performance vs hardware cost
- 128K context window
- 140 language coverage
- Strong general-purpose default
Limitations
- Gemma License rather than Apache
- At least 9GB RAM required for Ollama deployment
- No dedicated thinking mode
Typical workloads
In our catalog grid, Gemma 3 12B is filed under Versatile Chat, Vision, Translation — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents; multilingual workloads.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the Gemma license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Dense VLM · sliding-window attention · multimodal
Training: 12T tokens.
The pragmatic Gemma 3 — most teams should start here before reaching for the 27B.
Quick start
ollama run gemma3:12bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Gemma 3 12B need?
At the recommended Q4_K_M quantization, Gemma 3 12B needs about 7 GB of VRAM. Q8_0 takes 13 GB, and unquantized FP16 weights take 24 GB.
Can Gemma 3 12B run without a GPU?
Yes — with roughly 14 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Gemma 3 12B support?
Gemma 3 12B supports a 125k-token context window (128,000 tokens).
Can I use Gemma 3 12B commercially?
Gemma 3 12B ships under the Gemma license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Gemma 3 12B on consumer hardware?
Our compatibility engine estimates on the order of 22 tokens/sec on a mid-range GPU and up to 60 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Gemma 3 12B should I download first?
Start with Q4_K_M (7 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q4_K_M.