How much VRAM does gemma-3-12b-it-qat need?

Q4_0 is exactly 7,131,017,792 bytes (6.64 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is gemma-3-12b-it-qat's KV cache?

2.47 GiB at 32K context with an f16 cache, computed per layer from the model's architecture — most of its layers use sliding-window attention, so a flat formula overstates this substantially. Quantizing the cache to q8_0 roughly halves it.

google · text

gemma-3-12b-it-qat

google/gemma-3-12b-it-qat

gemma-3-12b-it-qat at Q4_0 is exactly 7,131,017,792 bytes (6.64 GiB / 7.13 GB) — an effective 4.848 bits per weight, not the nominal 4. Its KV cache at 32K is 2.47 GiB, not the 12.00 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV from mirror (mirror:unsloth/gemma-3-12b-it-qat)

Parameters

11.8B

Architecture

gemma3

48 layers

Context

131,072

native (config.json)

License

—

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q4_0	6.64 GiB	7,131,017,792	4.848	—	ggml-org

KV cache by context

computed per layer — this model uses sliding-window attention

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	0.72 GiB	1.50 GiB	2.09×	8 / 40 / 0
8,192	0.97 GiB	3.00 GiB	3.10×	8 / 40 / 0
16,384	1.47 GiB	6.00 GiB	4.09×	8 / 40 / 0
32,768	2.47 GiB	12.00 GiB	4.86×	8 / 40 / 0
65,536	4.47 GiB	24.00 GiB	5.37×	8 / 40 / 0
131,072	8.47 GiB	48.00 GiB	5.67×	8 / 40 / 0

40 of 48 layers cache only a 1,024-token window rather than the full context, on a period of 6. Figures assume the default configuration; --swa-full disables the saving entirely.

Compare with

same modality, comparable size

vs Qwen3-Coder-30B-A3B-Instruct vs Qwen3.6-27B vs Qwen3.8-27B vs gemma-4-12B-it-qat-q4_0-unquantized vs gemma-4-E4B-it vs Qwen3-30B-A3B-Thinking-2507 All alternatives →Will it fit on my card? →

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_0 at roughly 6.16 GiB. The real file is 6.64 GiB, because a quantization is a mixture and some tensors are always kept at higher precision. The larger discrepancy is the cache: a flat formula gives 12.00 GiB at 32K context where the real figure is 2.47 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from mirror:unsloth/gemma-3-12b-it-qat

Layers

Attention heads

KV heads

Head dim

256

Hidden size

3840

Vocab

262,208

Sliding window

1024

SWA period

MLA

Experts

—

Experts per token

—

use_sliding_window

—

Questions people ask

How much VRAM does gemma-3-12b-it-qat need?: Q4_0 is exactly 7,131,017,792 bytes (6.64 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is gemma-3-12b-it-qat's KV cache?: 2.47 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.
Which quantization of gemma-3-12b-it-qat should I use?: Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.