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gemma-4-E4B-it-QAT-SOMPOA-heresy

MuXodious/gemma-4-E4B-it-QAT-SOMPOA-heresy

gemma-4-E4B-it-QAT-SOMPOA-heresy at Q4_K_M is exactly 5,302,273,248 bytes (4.94 GiB / 5.30 GB) — an effective 5.342 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
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S3.06 GiB3,289,757,1843.314mradermacher
I1-IQ1_M3.14 GiB3,372,271,1043.397mradermacher
I1-IQ2_XXS3.27 GiB3,509,794,3043.536mradermacher
I1-IQ2_XS3.38 GiB3,627,841,0243.655mradermacher
I1-IQ2_S3.42 GiB3,677,279,7443.705mradermacher
I1-IQ2_M3.53 GiB3,787,298,3043.815mradermacher
I1-IQ3_XXS3.71 GiB3,982,841,3444.012mradermacher
I1-Q2_K_S3.98 GiB4,269,590,0164.301mradermacher
Q2_K4.08 GiB4,376,782,0484.409mradermacher
I1-Q2_K4.08 GiB4,376,782,3364.409mradermacher
I1-IQ3_XS4.23 GiB4,545,885,6964.580mradermacher
Q3_K_S4.31 GiB4,630,959,3284.665mradermacher
I1-Q3_K_S4.31 GiB4,630,959,6164.665mradermacher
I1-IQ3_S4.32 GiB4,635,833,8564.670mradermacher
I1-IQ3_M4.37 GiB4,687,361,5364.722mradermacher
Q3_K_M4.49 GiB4,823,061,7284.859mradermacher
I1-Q3_K_M4.49 GiB4,823,062,0164.859mradermacher
Q3_K_L4.65 GiB4,990,506,2085.027mradermacher
I1-Q3_K_L4.65 GiB4,990,506,4965.027mradermacher
I1-IQ4_XS4.69 GiB5,037,385,2165.075mradermacher
IQ4_XS4.71 GiB5,057,864,9285.095mradermacher
I1-IQ4_NL4.81 GiB5,159,527,9365.198mradermacher
I1-Q4_04.81 GiB5,163,132,4165.201mradermacher
Q4_K_S4.82 GiB5,171,979,4885.210mradermacher
I1-Q4_K_S4.82 GiB5,171,979,7765.210mradermacher
Q4_K_M4.94 GiB5,302,273,2485.342mradermacher
I1-Q4_K_M4.94 GiB5,302,273,5365.342mradermacher
I1-Q4_15.03 GiB5,401,519,6165.442mradermacher
Q5_K_S5.26 GiB5,648,098,5285.690mradermacher
I1-Q5_K_S5.26 GiB5,648,098,8165.690mradermacher
Q5_K_M5.33 GiB5,723,997,4085.766mradermacher
I1-Q5_K_M5.33 GiB5,723,997,6965.766mradermacher
Q6_K5.75 GiB6,172,079,3286.218mradermacher
I1-Q6_K5.75 GiB6,172,079,6166.218mradermacher
Q8_07.43 GiB7,972,725,9848.032mradermacher
F1613.92 GiB14,942,971,10415.054mradermacher

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 4.16 GiB. The real file is 4.94 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-QAT-SOMPOA-heresy need?
Q4_K_M is exactly 5,302,273,248 bytes (4.94 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-SOMPOA-heresy'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-SOMPOA-heresy 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.