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gemma-4-12b-heretic-styletune-head

jebcarter/gemma-4-12b-heretic-styletune-head

gemma-4-12b-heretic-styletune-head at Q4_K_M is exactly 7,947,613,344 bytes (7.40 GiB / 7.95 GB) — an effective 5.316 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
12.0B
Architecture
gemma4
48 layers
Context
131,072
native (config.json)
License

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S3.09 GiB3,313,993,1522.217mradermacher
I1-IQ1_M3.29 GiB3,533,149,6322.363mradermacher
I1-IQ2_XXS3.63 GiB3,898,410,4322.608mradermacher
I1-IQ2_XS3.93 GiB4,218,144,1922.822mradermacher
I1-IQ2_S4.20 GiB4,512,134,5923.018mradermacher
I1-IQ2_M4.47 GiB4,804,343,2323.214mradermacher
I1-Q2_K_S4.50 GiB4,834,448,8323.234mradermacher
Q2_K4.81 GiB5,160,449,1843.452mradermacher
I1-Q2_K4.81 GiB5,160,449,4723.452mradermacher
I1-IQ3_XXS4.92 GiB5,281,732,0323.533mradermacher
I1-IQ3_XS5.31 GiB5,704,930,7523.816mradermacher
Q3_K_S5.55 GiB5,960,766,6243.987mradermacher
I1-IQ3_S5.55 GiB5,960,766,9123.987mradermacher
I1-Q3_K_S5.55 GiB5,960,766,9123.987mradermacher
I1-IQ3_M5.74 GiB6,166,529,4724.125mradermacher
Q3_K_M6.07 GiB6,519,624,8644.361mradermacher
I1-Q3_K_M6.07 GiB6,519,625,1524.361mradermacher
Q3_K_L6.52 GiB6,998,856,8644.682mradermacher
I1-Q3_K_L6.52 GiB6,998,857,1524.682mradermacher
I1-IQ4_XS6.68 GiB7,170,028,9924.796mradermacher
IQ4_XS6.73 GiB7,225,324,7044.833mradermacher
I1-IQ4_NL7.02 GiB7,542,109,6325.045mradermacher
I1-Q4_07.04 GiB7,564,228,0325.060mradermacher
Q4_K_S7.07 GiB7,590,278,3045.077mradermacher
I1-Q4_K_S7.07 GiB7,590,278,5925.077mradermacher
Q4_K_M7.40 GiB7,947,613,3445.316mradermacher
I1-Q4_K_M7.40 GiB7,947,613,6325.316mradermacher
I1-Q4_17.72 GiB8,286,270,9125.543mradermacher
Q5_K_S8.41 GiB9,030,431,9046.041mradermacher
I1-Q5_K_S8.41 GiB9,030,432,1926.041mradermacher
Q5_K_M8.60 GiB9,239,327,9046.180mradermacher
I1-Q5_K_M8.60 GiB9,239,328,1926.180mradermacher
Q6_K9.88 GiB10,611,774,6247.098mradermacher
I1-Q6_K9.88 GiB10,611,774,9127.098mradermacher
Q8_012.80 GiB13,739,193,5049.190mradermacher

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 . 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 6.27 GiB. The real file is 7.40 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
262,144
Sliding window
1024
SWA period
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does gemma-4-12b-heretic-styletune-head need?
Q4_K_M is exactly 7,947,613,344 bytes (7.40 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-4-12b-heretic-styletune-head'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-4-12b-heretic-styletune-head 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.