How much VRAM does gemma-4-E4B-it-qat-q4_0-unquantized need?

Q4_0 is exactly 5,154,940,992 bytes (4.80 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is gemma-4-E4B-it-qat-q4_0-unquantized's KV cache?

0.51 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 · vision language

gemma-4-E4B-it-qat-q4_0-unquantized

google/gemma-4-E4B-it-qat-q4_0-unquantized

gemma-4-E4B-it-qat-q4_0-unquantized at Q4_0 is exactly 5,154,940,992 bytes (4.80 GiB / 5.15 GB) — an effective 5.193 bits per weight, not the nominal 4. Its KV cache at 32K is 0.51 GiB, not the 2.63 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer

Parameters

7.9B

Architecture

gemma4

42 layers

Context

131,072

native (config.json)

License

apache-2.0

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q4_0	4.80 GiB	5,154,940,992	5.193	666	lmstudio-community

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	0.12 GiB	0.33 GiB	2.67×	7 / 35 / 0
8,192	0.18 GiB	0.66 GiB	3.69×	7 / 35 / 0
16,384	0.29 GiB	1.31 GiB	4.57×	7 / 35 / 0
32,768	0.51 GiB	2.63 GiB	5.19×	7 / 35 / 0
65,536	0.94 GiB	5.25 GiB	5.57×	7 / 35 / 0
131,072	1.82 GiB	10.50 GiB	5.77×	7 / 35 / 0

35 of 42 layers cache only a 512-token window rather than the full context, on a period of . Figures assume the default configuration; --swa-full disables the saving entirely.

Compare with

same modality, comparable size

vs Qwen3.6-35B-A3B vs Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-MTP vs Qwen3.5-9B vs gemma-4-26B-A4B-it vs gemma-4-12B-it vs Qwen3.5-4B 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 4.16 GiB. The real file is 4.80 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 2.63 GiB at 32K context where the real figure is 0.51 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json

Layers

Attention heads

KV heads

Head dim

256

Hidden size

2560

Vocab

262,144

Sliding window

512

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

—

Questions people ask

How much VRAM does gemma-4-E4B-it-qat-q4_0-unquantized need?: Q4_0 is exactly 5,154,940,992 bytes (4.80 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-4-E4B-it-qat-q4_0-unquantized's KV cache?: 0.51 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-4-E4B-it-qat-q4_0-unquantized 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.