WWTCyberLab · text

gemma-4-E4B-it-abliterated

WWTCyberLab/gemma-4-E4B-it-abliterated

gemma-4-E4B-it-abliterated at I1-IQ1_S is exactly 3,289,758,368 bytes (3.06 GiB / 3.29 GB) — an effective 3.291 bits per weight, not the nominal 1. 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
gemma

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S3.06 GiB3,289,758,3683.291mradermacher
I1-IQ1_M3.14 GiB3,372,272,2883.374mradermacher
I1-IQ2_XXS3.27 GiB3,509,795,4883.511mradermacher
I1-IQ2_XS3.38 GiB3,627,842,2083.630mradermacher
I1-IQ2_S3.42 GiB3,677,280,9283.679mradermacher
I1-IQ2_M3.53 GiB3,787,299,4883.789mradermacher
I1-IQ3_XXS3.71 GiB3,982,842,5283.985mradermacher
I1-Q2_K_S3.98 GiB4,269,591,2004.272mradermacher
I1-Q2_K4.10 GiB4,401,316,5444.403mradermacher
I1-IQ3_XS4.23 GiB4,545,886,8804.548mradermacher
I1-Q3_K_S4.33 GiB4,654,633,6644.657mradermacher
I1-IQ3_S4.34 GiB4,663,163,5844.665mradermacher
I1-IQ3_M4.39 GiB4,714,691,2644.717mradermacher
I1-Q3_K_M4.52 GiB4,850,391,7444.853mradermacher
I1-Q3_K_L4.68 GiB5,021,276,8645.024mradermacher
I1-IQ4_XS4.72 GiB5,070,951,1045.073mradermacher
I1-IQ4_NL4.84 GiB5,193,953,9845.197mradermacher
I1-Q4_04.84 GiB5,194,117,8245.197mradermacher
I1-Q4_K_S4.85 GiB5,202,965,1845.205mradermacher
I1-Q4_K_M4.97 GiB5,335,286,4645.338mradermacher
I1-Q4_15.06 GiB5,435,945,6645.439mradermacher
I1-Q5_K_S5.30 GiB5,685,965,5045.689mradermacher
I1-Q5_K_M5.37 GiB5,762,908,8645.766mradermacher
I1-Q6_K5.79 GiB6,217,257,6646.220mradermacher

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 I1-IQ1_S at roughly 4.19 GiB. The real file is 3.06 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-abliterated need?
I1-IQ1_S is exactly 3,289,758,368 bytes (3.06 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-abliterated'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-abliterated 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.