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gemma-4-12B-it-Tachibana-Agent

sequelbox/gemma-4-12B-it-Tachibana-Agent

gemma-4-12B-it-Tachibana-Agent at Q4_K_M is exactly 7,381,383,520 bytes (6.87 GiB / 7.38 GB) — an effective 4.938 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
262,144
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S2.78 GiB2,983,692,9281.996mradermacher
I1-IQ1_M2.98 GiB3,202,849,4082.142mradermacher
I1-IQ2_XXS3.32 GiB3,568,110,2082.387mradermacher
I1-IQ2_XS3.62 GiB3,887,843,9682.601mradermacher
I1-IQ2_S3.80 GiB4,079,598,2082.729mradermacher
I1-IQ2_M4.07 GiB4,371,806,8482.924mradermacher
I1-Q2_K_S4.19 GiB4,504,148,6083.013mradermacher
Q2_K4.50 GiB4,830,148,9603.231mradermacher
I1-Q2_K4.50 GiB4,830,149,2483.231mradermacher
I1-IQ3_XXS4.52 GiB4,849,195,6483.244mradermacher
I1-IQ3_XS4.91 GiB5,272,394,3683.527mradermacher
Q3_K_S5.15 GiB5,528,230,2403.698mradermacher
I1-Q3_K_S5.15 GiB5,528,230,5283.698mradermacher
I1-IQ3_S5.15 GiB5,528,230,5283.698mradermacher
I1-IQ3_M5.34 GiB5,733,993,0883.836mradermacher
Q3_K_M5.67 GiB6,087,088,4804.072mradermacher
I1-Q3_K_M5.67 GiB6,087,088,7684.072mradermacher
Q3_K_L6.12 GiB6,566,320,4804.392mradermacher
I1-Q3_K_L6.12 GiB6,566,320,7684.392mradermacher
I1-IQ4_XS6.18 GiB6,635,256,4484.438mradermacher
IQ4_XS6.23 GiB6,690,552,1604.475mradermacher
I1-IQ4_NL6.50 GiB6,975,879,8084.666mradermacher
I1-Q4_06.52 GiB6,997,998,2084.681mradermacher
Q4_K_S6.54 GiB7,024,048,4804.699mradermacher
I1-Q4_K_S6.54 GiB7,024,048,7684.699mradermacher
Q4_K_M6.87 GiB7,381,383,5204.938mradermacher
I1-Q4_K_M6.87 GiB7,381,383,8084.938mradermacher
I1-Q4_17.13 GiB7,657,126,5285.122mradermacher
Q5_K_S7.77 GiB8,338,372,9605.578mradermacher
I1-Q5_K_S7.77 GiB8,338,373,2485.578mradermacher
Q5_K_M7.96 GiB8,547,268,9605.717mradermacher
I1-Q5_K_M7.96 GiB8,547,269,2485.717mradermacher
Q6_K9.11 GiB9,786,022,2406.546mradermacher
I1-Q6_K9.11 GiB9,786,022,5286.546mradermacher
Q8_011.80 GiB12,669,647,2008.475mradermacher

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 6.87 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-it-Tachibana-Agent need?
Q4_K_M is exactly 7,381,383,520 bytes (6.87 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-it-Tachibana-Agent'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-it-Tachibana-Agent 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.