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gemma-4-E4B-it

google/gemma-4-E4B-it

gemma-4-E4B-it at Q4_K_M is exactly 4,977,171,584 bytes (4.64 GiB / 4.98 GB) — an effective 4.980 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
8.0B
Architecture
gemma4
42 layers
Context
131,072
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
UD-IQ2_M3.30 GiB3,545,083,0083.547unsloth
UD-IQ3_XXS3.46 GiB3,717,115,0083.719unsloth
Q3_K_S3.60 GiB3,862,379,6483.864unsloth
Q3_K_M3.78 GiB4,058,137,7284.060720unsloth
Q4_02 shards4.33 GiB4,650,485,6324.653ggml-org
IQ3_M4.39 GiB4,714,690,5284.717stronman
IQ3_M4.39 GiB4,714,690,5284.717HauhauCS
IQ3_M4.39 GiB4,714,690,5284.717keyserkazi
IQ4_XS4.39 GiB4,715,416,7044.718720unsloth
IQ4_NL4.50 GiB4,835,839,1044.838unsloth
Q4_04.50 GiB4,836,002,9444.838720unsloth
Q4_K_S4.51 GiB4,844,850,3044.847unsloth
Q3_K_M4.52 GiB4,850,391,0084.853stronman
Q3_K_M4.52 GiB4,850,391,0084.853HauhauCS
Q3_K_M4.52 GiB4,850,391,0084.853keyserkazi
Q4_K_M4.64 GiB4,977,171,5844.980720unsloth
IQ4_XS4.72 GiB5,070,950,3685.073stronman
IQ4_XS4.72 GiB5,070,950,3685.073keyserkazi
IQ4_XS4.72 GiB5,070,950,3685.073HauhauCS
Q4_14.73 GiB5,074,390,1445.077unsloth
Q4_K_M4.97 GiB5,335,285,7285.338keyserkazi
Q4_K_M4.97 GiB5,335,285,7285.338stronman
Q4_K_M4.97 GiB5,335,285,7285.338HauhauCS
Q4_K_M4.97 GiB5,335,291,9365.338720lmstudio-community
Q5_K_S5.03 GiB5,404,855,4245.407unsloth
Q5_K_M5.11 GiB5,481,798,7845.484720unsloth
Q5_K_M5.37 GiB5,762,908,1285.766stronman
Q5_K_M5.37 GiB5,762,908,1285.766keyserkazi
Q5_K_M5.37 GiB5,762,908,1285.766HauhauCS
Q6_K5.79 GiB6,217,260,8646.220720lmstudio-community
Q6_K6.59 GiB7,074,929,7927.078unsloth
Q8_07.48 GiB8,031,240,0008.035720lmstudio-community
Q8_02 shards7.57 GiB8,129,895,9688.134ggml-org
Q8_07.63 GiB8,192,953,4728.197unsloth
BF1614.02 GiB15,053,097,85615.060unsloth
BF162 shards14.18 GiB15,224,864,28815.232ggml-org

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.12 GiB0.33 GiB2.67×7 / 35 / 0
8,1920.18 GiB0.66 GiB3.69×7 / 35 / 0
16,3840.29 GiB1.31 GiB4.57×7 / 35 / 0
32,7680.51 GiB2.63 GiB5.19×7 / 35 / 0
65,5360.94 GiB5.25 GiB5.57×7 / 35 / 0
131,0721.82 GiB10.50 GiB5.77×7 / 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

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 4.19 GiB. The real file is 4.64 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 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
42
Attention heads
8
KV heads
2
Head dim
256
Hidden size
2560
Vocab
262,144
Sliding window
512
SWA period
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does gemma-4-E4B-it need?
Q4_K_M is exactly 4,977,171,584 bytes (4.64 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-4-E4B-it'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 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.