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gemma-3-270m

google/gemma-3-270m

gemma-3-270m at Q4_K_M is exactly 506,228,096 bytes (0.47 GiB / 0.51 GB) — an effective 15.106 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 2 file(s)From the file· KV from mirror (mirror:unsloth/gemma-3-270m)
Parameters
268M
Architecture
gemma3
18 layers
Context
32,768
native (config.json)
License
gemma

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
IQ1_M0.22 GiB237,077,4087.074gguf-org
IQ2_XXS0.22 GiB237,077,4087.074gguf-org
IQ1_S0.22 GiB237,077,4087.074gguf-org
IQ2_S0.22 GiB237,077,4087.074gguf-org
IQ2_XS0.22 GiB237,077,4087.074gguf-org
Q8_00.27 GiB291,543,7448.700ggml-org
Q3_K_S2 shards0.44 GiB473,419,13614.127gguf-org
Q2_K2 shards0.44 GiB474,156,41614.149gguf-org
IQ3_S2 shards0.44 GiB474,156,41614.149gguf-org
Q2_K_S2 shards0.44 GiB474,156,41614.149gguf-org
IQ3_XXS2 shards0.44 GiB474,852,73614.169gguf-org
IQ4_XS2 shards0.45 GiB482,532,73614.399gguf-org
Q4_02 shards0.45 GiB482,819,45614.407gguf-org
Q3_K_M2 shards0.45 GiB483,925,37614.440gguf-org
IQ4_NL2 shards0.45 GiB484,580,73614.460gguf-org
Q3_K_L2 shards0.46 GiB492,772,73614.704gguf-org
Q4_12 shards0.46 GiB495,353,21614.781gguf-org
Q4_K_S2 shards0.47 GiB499,776,89614.913gguf-org
Q4_K_M2 shards0.47 GiB506,228,09615.106gguf-org
Q5_02 shards0.47 GiB507,886,97615.155gguf-org
Q5_K_S2 shards0.48 GiB515,997,05615.397gguf-org
Q5_K_M2 shards0.48 GiB520,052,09615.518gguf-org
Q5_12 shards0.48 GiB520,420,73615.529gguf-org
F160.51 GiB542,834,84816.198pixasocial
Q6_K2 shards0.53 GiB565,947,77616.888gguf-org
Q8_02 shards0.54 GiB583,089,53617.399gguf-org
F321.00 GiB1,078,919,32832.195pixasocial
BF162 shards1.01 GiB1,085,668,73632.396gguf-org
F162 shards1.01 GiB1,085,668,73632.396gguf-org
F322 shards2.01 GiB2,157,837,696gguf-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_K_M at roughly 0.14 GiB. The real file is 0.47 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
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 need?
Q4_K_M is exactly 506,228,096 bytes (0.47 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'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 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.