google · text

gemma-3-270m-it-qat

google/gemma-3-270m-it-qat

gemma-3-270m-it-qat at Q4_0 is exactly 241,410,624 bytes (0.22 GiB / 0.24 GB) — an effective 7.204 bits per weight, not the nominal 4. Its KV cache at 32K is 0.11 GiB, not the 0.56 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV from mirror (mirror:unsloth/gemma-3-270m-it-qat)
Parameters
268M
Architecture
gemma3
18 layers
Context
32,768
native (config.json)
License

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q4_00.22 GiB241,410,6247.204ggml-org

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.03 GiB0.07 GiB2.67×3 / 15 / 0
8,1920.04 GiB0.14 GiB3.69×3 / 15 / 0
16,3840.06 GiB0.28 GiB4.57×3 / 15 / 0
32,7680.11 GiB0.56 GiB5.19×3 / 15 / 0
65,5360.20 GiB1.13 GiB5.57×3 / 15 / 0
131,0720.39 GiB2.25 GiB5.77×3 / 15 / 0

15 of 18 layers cache only a 512-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

Will it run on your card?

full quant x context sweep

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_0 at roughly 0.14 GiB. The real file is 0.22 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 0.56 GiB at 32K context where the real figure is 0.11 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from mirror:unsloth/gemma-3-270m-it-qat
Layers
18
Attention heads
4
KV heads
1
Head dim
256
Hidden size
640
Vocab
262,144
Sliding window
512
SWA period
6
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does gemma-3-270m-it-qat need?
Q4_0 is exactly 241,410,624 bytes (0.22 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-3-270m-it-qat's KV cache?
0.11 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-3-270m-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.