coder3101 · vision language

gemma-4-E2B-it-qat-q4_0-unquantized-heretic

coder3101/gemma-4-E2B-it-qat-q4_0-unquantized-heretic

gemma-4-E2B-it-qat-q4_0-unquantized-heretic at Q4_K_M is exactly 3,416,119,296 bytes (3.18 GiB / 3.42 GB) — an effective 5.354 bits per weight, not the nominal 4. Its KV cache at 32K is 0.25 GiB, not the 1.09 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
5.1B
Architecture
gemma4
35 layers
Context
131,072
native (config.json)
License
gemma

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S2.16 GiB2,315,865,4083.630mradermacher
I1-IQ1_M2.19 GiB2,355,383,6163.692mradermacher
I1-IQ2_XXS2.25 GiB2,421,247,2963.795mradermacher
I1-IQ2_XS2.31 GiB2,478,067,0083.884mradermacher
I1-IQ2_S2.33 GiB2,500,480,3203.919mradermacher
I1-IQ2_M2.38 GiB2,553,171,2644.002mradermacher
I1-IQ3_XXS2.47 GiB2,648,083,7764.150mradermacher
I1-Q2_K_S2.72 GiB2,923,367,7444.582mradermacher
Q2_K2.78 GiB2,980,654,0804.672mradermacher
I1-Q2_K2.78 GiB2,980,654,4004.672mradermacher
I1-IQ3_XS2.85 GiB3,060,219,2004.796mradermacher
Q3_K_S2.89 GiB3,102,077,9524.862mradermacher
I1-Q3_K_S2.89 GiB3,102,078,2724.862mradermacher
I1-IQ3_S2.89 GiB3,103,018,3044.863mradermacher
I1-IQ3_M2.91 GiB3,125,579,0724.899mradermacher
Q3_K_M2.97 GiB3,191,958,5285.003mradermacher
I1-Q3_K_M2.97 GiB3,191,958,8485.003mradermacher
Q3_K_L3.05 GiB3,271,781,3765.128mradermacher
I1-Q3_K_L3.05 GiB3,271,781,6965.128mradermacher
I1-IQ4_XS3.07 GiB3,292,400,9605.160mradermacher
IQ4_XS3.07 GiB3,298,298,8805.169mradermacher
I1-IQ4_NL3.12 GiB3,350,400,3205.251mradermacher
I1-Q4_03.12 GiB3,351,874,8805.253mradermacher
Q4_K_S3.12 GiB3,354,430,4645.257mradermacher
I1-Q4_K_S3.12 GiB3,354,430,7845.257mradermacher
Q4_K_M3.18 GiB3,416,119,2965.354mradermacher
I1-Q4_K_M3.18 GiB3,416,119,6165.354mradermacher
I1-Q4_13.23 GiB3,465,956,6725.432mradermacher

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.05 GiB0.14 GiB2.50×7 / 28 / 0
8,1920.08 GiB0.27 GiB3.33×7 / 28 / 0
16,3840.14 GiB0.55 GiB4.00×7 / 28 / 0
32,7680.25 GiB1.09 GiB4.44×7 / 28 / 0
65,5360.46 GiB2.19 GiB4.71×7 / 28 / 0
131,0720.90 GiB4.38 GiB4.85×7 / 28 / 0

28 of 35 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 2.67 GiB. The real file is 3.18 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 1.09 GiB at 32K context where the real figure is 0.25 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json
Layers
35
Attention heads
8
KV heads
1
Head dim
256
Hidden size
1536
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-E2B-it-qat-q4_0-unquantized-heretic need?
Q4_K_M is exactly 3,416,119,296 bytes (3.18 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-E2B-it-qat-q4_0-unquantized-heretic's KV cache?
0.25 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-E2B-it-qat-q4_0-unquantized-heretic 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.