Gemma 4 12B
By Google · United States
Updated 2026-08-31
Overview
Google's Gemma 4 12B — a dense, multimodal (text/vision/audio) model with a 256k-token context, Apache 2.0 licensed, and light enough to run at ~7GB VRAM in Q4.
When to pick this model
- Local multimodal apps needing text, image, and audio input in one model
- Multilingual assistants that need commercial-friendly licensing
- Long-document or long-transcript analysis up to 256k tokens
- Single-GPU deployments where a 12B footprint is a hard constraint
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 4 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 4 12B needs roughly 16 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 18 tokens/sec on entry-level GPUs, on the order of 28 tokens/sec on a mid-range card, and up to 45 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Gemma 4 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 4 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 hardware should you buy to run Gemma 4 12B?
To run Gemma 4 12B locally at Q4, you need ~7 GB of VRAM. The best value for this today is a RTX 5060 Ti 16GB (ASUS Dual OC) (16 GB VRAM, best $/GB).
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Strengths
- Handles text, vision, and audio input natively
- 256k-token context window
- Apache 2.0, cleared for commercial use
- Runs on ~7GB VRAM at Q4
Limitations
- Dense architecture trades some speed versus a similarly sized MoE
- Throughput is only moderate on entry-level GPUs
Typical workloads
In our catalog grid, Gemma 4 12B is filed under Local Multimodal, Multilingual, Long Context — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multi-step reasoning and math-flavoured tasks; vision-language work — screenshots, charts, scanned documents; audio understanding; multilingual workloads.
The 256k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense transformer, 12B parameters · 256k context · multimodal
Training: Google, Gemma 4 family. Multimodal inputs (text, image, audio) and multilingual support. Apache 2.0 license.
A compact, Apache-licensed multimodal model that punches above its 12B weight for local text/vision/audio work.
Quick start
# HuggingFace : google/gemma-4-12BOr 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 4 12B need?
At the recommended Q4_K_M quantization, Gemma 4 12B needs about 7 GB of VRAM. Q8_0 takes 13 GB, and unquantized FP16 weights take 24 GB.
Can Gemma 4 12B run without a GPU?
Yes — with roughly 16 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 4 12B support?
Gemma 4 12B supports a 256k-token context window (262,144 tokens).
Can I use Gemma 4 12B commercially?
Yes. Gemma 4 12B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Gemma 4 12B on consumer hardware?
Our compatibility engine estimates on the order of 28 tokens/sec on a mid-range GPU and up to 45 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Gemma 4 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.