mlabonne · vision language

gemma-3-4b-it-abliterated

mlabonne/gemma-3-4b-it-abliterated

gemma-3-4b-it-abliterated at Q4_K_M is exactly 2,489,894,304 bytes (2.32 GiB / 2.49 GB) — an effective 4.632 bits per weight, not the nominal 4. Its KV cache at 32K is 0.79 GiB, not the 4.25 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
4.3B
Architecture
gemma3
34 layers
Context
131,072
native (config.json)
License
gemma

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
IQ2_M1.43 GiB1,537,982,6242.861bartowski
IQ3_XXS1.57 GiB1,689,452,7043.143bartowski
Q2_K1.61 GiB1,729,164,7043.217bartowski
Q2_K1.61 GiB1,729,164,8643.217mradermacher
IQ3_XS1.74 GiB1,863,390,6243.467bartowski
Q2_K_L1.76 GiB1,891,733,6643.519bartowski
Q3_K_S1.80 GiB1,937,364,3843.604bartowski
Q3_K_S1.80 GiB1,937,364,5443.604mradermacher
IQ3_M1.85 GiB1,986,803,1043.696bartowski
Q3_K_M1.95 GiB2,098,460,0643.904bartowski
Q3_K_M1.95 GiB2,098,460,2243.904mradermacher
Q3_K_L2.08 GiB2,236,085,6644.160bartowski
Q3_K_L2.08 GiB2,236,085,8244.160mradermacher
IQ4_XS2.11 GiB2,263,242,1444.211bartowski
IQ4_XS2.12 GiB2,279,626,3044.241mradermacher
IQ4_NL2.20 GiB2,363,512,2244.397bartowski
Q4_02.21 GiB2,370,065,8244.409bartowski
Q4_K_S2.21 GiB2,377,930,1444.424bartowski
Q4_K_S2.21 GiB2,377,930,3044.424mradermacher
Q4_K_M2.32 GiB2,489,894,3044.632bartowski
Q4_K_M2.32 GiB2,489,894,4644.632mradermacher
Q4_12.39 GiB2,564,052,3844.770bartowski
Q4_K_L2.47 GiB2,652,463,2644.935bartowski
Q5_K_S2.57 GiB2,764,592,5445.143bartowski
Q5_K_S2.57 GiB2,764,592,7045.143mradermacher
Q5_K_M2.64 GiB2,829,698,4645.264bartowski
Q5_K_M2.64 GiB2,829,698,6245.264mradermacher
Q5_K_L2.79 GiB2,992,267,4245.567bartowski
Q6_K2.97 GiB3,190,740,3845.936bartowski
Q6_K2.97 GiB3,190,740,5445.936mradermacher
Q6_K_L3.12 GiB3,353,309,3446.239bartowski
Q8_03.85 GiB4,130,402,4647.684bartowski
Q8_03.85 GiB4,130,402,6247.684mradermacher
BF167.23 GiB7,767,803,77614.451bartowski
F167.23 GiB7,767,804,22414.451mradermacher

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.25 GiB0.53 GiB2.14×5 / 29 / 0
8,1920.33 GiB1.06 GiB3.26×5 / 29 / 0
16,3840.48 GiB2.13 GiB4.40×5 / 29 / 0
32,7680.79 GiB4.25 GiB5.35×5 / 29 / 0
65,5361.42 GiB8.50 GiB5.99×5 / 29 / 0
131,0722.67 GiB17.00 GiB6.37×5 / 29 / 0

29 of 34 layers cache only a 1,024-token window rather than the full context, on a period of 6. 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 2.25 GiB. The real file is 2.32 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 4.25 GiB at 32K context where the real figure is 0.79 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json
Layers
34
Attention heads
8
KV heads
4
Head dim
256
Hidden size
2560
Vocab
262,208
Sliding window
1024
SWA period
6
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does gemma-3-4b-it-abliterated need?
Q4_K_M is exactly 2,489,894,304 bytes (2.32 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-3-4b-it-abliterated's KV cache?
0.79 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-3-4b-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.