prithivMLmods · text

gemma-4-E2B-it-Uncensored-MAX

prithivMLmods/gemma-4-E2B-it-Uncensored-MAX

gemma-4-E2B-it-Uncensored-MAX at Q4_K_M is exactly 3,427,874,432 bytes (3.19 GiB / 3.43 GB) — an effective 5.353 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
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q2_K2.78 GiB2,989,082,2404.668mradermacher
I1-Q2_K2.78 GiB2,989,082,5284.668mradermacher
Q3_K_S2.90 GiB3,110,211,2004.857mradermacher
I1-Q3_K_S2.90 GiB3,110,211,4884.857mradermacher
I1-IQ3_S2.90 GiB3,112,404,8964.860mradermacher
I1-IQ3_M2.92 GiB3,134,965,6644.895mradermacher
Q3_K_M2.98 GiB3,201,345,1524.999mradermacher
I1-Q3_K_M2.98 GiB3,201,345,4404.999mradermacher
Q3_K_L3.06 GiB3,282,347,6485.125mradermacher
I1-Q3_K_L3.06 GiB3,282,347,9365.125mradermacher
I1-IQ4_XS3.08 GiB3,303,925,6645.159mradermacher
IQ4_XS3.08 GiB3,309,823,6165.168mradermacher
I1-IQ4_NL3.13 GiB3,362,219,9365.250mradermacher
I1-Q4_03.13 GiB3,362,514,8485.251mradermacher
Q4_K_S3.13 GiB3,365,070,4645.255mradermacher
I1-Q4_K_S3.13 GiB3,365,070,7525.255mradermacher
Q4_K_M3.19 GiB3,427,874,4325.353mradermacher
I1-Q4_K_M3.19 GiB3,427,874,7205.353mradermacher
I1-Q4_13.24 GiB3,477,776,2885.431mradermacher
Q5_K_S3.35 GiB3,595,396,7365.614mradermacher
I1-Q5_K_S3.35 GiB3,595,397,0245.614mradermacher
Q5_K_M3.38 GiB3,630,282,3685.669mradermacher
I1-Q5_K_M3.38 GiB3,630,282,6565.669mradermacher
Q6_K3.58 GiB3,845,340,8006.005mradermacher
I1-Q6_K3.58 GiB3,845,341,0886.005mradermacher
Q8_04.61 GiB4,954,587,7447.737prithivMLmods
Q8_04.63 GiB4,967,491,2007.757mradermacher
BF168.67 GiB9,311,298,14414.540prithivMLmods
F168.67 GiB9,311,298,14414.540prithivMLmods
F168.67 GiB9,311,299,20014.540mradermacher
F3217.33 GiB18,605,613,66429.053prithivMLmods

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 2.68 GiB. The real file is 3.19 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-Uncensored-MAX need?
Q4_K_M is exactly 3,427,874,432 bytes (3.19 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-Uncensored-MAX'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-Uncensored-MAX 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.