How much VRAM does gemma-4-26B-A4B-it need?

Q8_0 is exactly 26,859,858,976 bytes (25.02 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is gemma-4-26B-A4B-it's KV cache?

1.54 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.

Is gemma-4-26B-A4B-it a mixture-of-experts model?

Yes — 128 experts with null routed per token. Every expert must be resident in memory, but only the routed ones are read per token, so its memory requirement and its speed behave very differently.

unsloth · text · mixture of experts

gemma-4-26B-A4B-it

unsloth/gemma-4-26B-A4B-it

gemma-4-26B-A4B-it at Q8_0 is exactly 26,859,858,976 bytes (25.02 GiB / 26.86 GB) — an effective 8.095 bits per weight, not the nominal 8. Its KV cache at 32K is 1.54 GiB, not the 7.50 GiB a flat formula predicts.

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

Parameters

26.5B

total, not active

Architecture

gemma4

30 layers

Context

262,144

native (config.json)

License

apache-2.0

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q8_0	25.02 GiB	26,859,858,976	8.095	—	terrorswift
F16	47.04 GiB	50,505,135,136	15.222	—	terrorswift

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	0.45 GiB	0.94 GiB	2.09×	5 / 25 / 0
8,192	0.61 GiB	1.88 GiB	3.10×	5 / 25 / 0
16,384	0.92 GiB	3.75 GiB	4.09×	5 / 25 / 0
32,768	1.54 GiB	7.50 GiB	4.86×	5 / 25 / 0
65,536	2.79 GiB	15.00 GiB	5.37×	5 / 25 / 0
131,072	5.29 GiB	30.00 GiB	5.67×	5 / 25 / 0

25 of 30 layers cache only a 1,024-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-Coder-30B-A3B-Instruct vs Qwen3.6-27B vs Qwen3.8-27B vs gemma-4-E4B-it vs Qwen3-30B-A3B-Thinking-2507 vs Qwen3-8B All alternatives →Will it fit on my card? →

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q8_0 at roughly 13.91 GiB. The real file is 25.02 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 7.50 GiB at 32K context where the real figure is 1.54 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

2816

Vocab

262,144

Sliding window

1024

SWA period

—

MLA

Experts

128

Experts per token

—

use_sliding_window

—

Questions people ask

How much VRAM does gemma-4-26B-A4B-it need?: Q8_0 is exactly 26,859,858,976 bytes (25.02 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-4-26B-A4B-it's KV cache?: 1.54 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.
Is gemma-4-26B-A4B-it a mixture-of-experts model?: Yes — 128 experts, null routed per token. Every expert must be resident, but only the routed ones are read per token, which is why its memory requirement and its speed behave very differently.
Which quantization of gemma-4-26B-A4B-it 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.