mlabonne · vision language

gemma-3-12b-it-abliterated-v2

mlabonne/gemma-3-12b-it-abliterated-v2

gemma-3-12b-it-abliterated-v2 at Q4_K_M is exactly 7,300,778,048 bytes (6.80 GiB / 7.30 GB) — an effective 4.964 bits per weight, not the nominal 4. Its KV cache at 32K is 2.47 GiB, not the 12.00 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
11.8B
Architecture
gemma3
48 layers
Context
131,072
native (config.json)
License
gemma

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
Q2_K4.44 GiB4,768,221,2483.242—mlabonne
Q2_K4.44 GiB4,768,221,6003.242—DevQuasar
Q3_K_S5.08 GiB5,458,315,6803.711—DevQuasar
Q3_K_M5.60 GiB6,008,817,7284.085—mlabonne
Q3_K_M5.60 GiB6,008,818,0804.085—DevQuasar
Q3_K_L6.04 GiB6,480,185,7604.406—DevQuasar
Q4_K_S6.46 GiB6,935,333,2804.715—DevQuasar
Q4_K_M6.80 GiB7,300,778,0484.964—mlabonne
Q4_K_M6.80 GiB7,300,778,4004.964—DevQuasar
Q4_K_M6.80 GiB7,300,778,4324.964—mvyzjoph
Q5_K_S7.67 GiB8,231,963,0405.597—DevQuasar
Q5_K_M7.87 GiB8,445,036,6085.742—mlabonne
Q5_K_M7.87 GiB8,445,036,9605.742—DevQuasar
Q6_K9.00 GiB9,660,811,3286.569—mlabonne
Q6_K9.00 GiB9,660,811,6806.569—DevQuasar
Q8_011.65 GiB12,510,212,2888.506—mlabonne
Q8_011.65 GiB12,510,212,6408.506—DevQuasar
F1621.92 GiB23,540,151,84016.006—DevQuasar

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)●Flat formulaOverstated byFull / windowed / recurrent
4,0960.72 GiB1.50 GiB2.09×8 / 40 / 0
8,1920.97 GiB3.00 GiB3.10×8 / 40 / 0
16,3841.47 GiB6.00 GiB4.09×8 / 40 / 0
32,7682.47 GiB12.00 GiB4.86×8 / 40 / 0
65,5364.47 GiB24.00 GiB5.37×8 / 40 / 0
131,0728.47 GiB48.00 GiB5.67×8 / 40 / 0

40 of 48 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
vs Qwen3.6-35B-A3Bvs Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-MTPvs Qwen3.5-9Bvs gemma-4-26B-A4B-itvs gemma-4-12B-itvs Qwen3.5-4BAll alternatives →Will it fit on my card? →

Will it run on your card?

full quant x context sweep
By VRAM:8GB12GB16GB24GB32GB48GB
Radeon RX 6500 XT 4GBGeForce RTX 3050 6GBGeForce RTX 5050 8GBGeForce RTX 3080 10GBGeForce RTX 2080 Ti 11GBGeForce RTX 5070 12GBGeForce RTX 5060 Ti 16GBApple M3 Pro 18GBGeForce RTX 3080 Ti 20GBGeForce RTX 5090 D V2 24GBGeForce RTX 5090 D 32GBApple M5 Max 36GBApple M5 Pro 48GBApple M5 Max 64GBApple M3 Ultra 96GBApple M5 Max 128GBApple M2 Ultra 192GBApple M3 Ultra 256GBApple M3 Ultra 512GB

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 6.16 GiB. The real file is 6.80 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 12.00 GiB at 32K context where the real figure is 2.47 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json
Layers
48
Attention heads
16
KV heads
8
Head dim
256
Hidden size
3840
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-3-12b-it-abliterated-v2 need?
Q4_K_M is exactly 7,300,778,048 bytes (6.80 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-3-12b-it-abliterated-v2's KV cache?
2.47 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-3-12b-it-abliterated-v2 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.