Ryex · text

Floppa-12B-Gemma3-Uncensored

Ryex/Floppa-12B-Gemma3-Uncensored

Floppa-12B-Gemma3-Uncensored at Q4_K_M is exactly 7,300,779,040 bytes (6.80 GiB / 7.30 GB) — an effective 4.792 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
12.2B
Architecture
gemma3
48 layers
Context
131,072
native (config.json)
License
gemma

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S2.74 GiB2,947,415,4881.935mradermacher
I1-IQ1_M2.95 GiB3,164,728,7682.077mradermacher
I1-IQ2_XXS3.28 GiB3,526,917,5682.315mradermacher
I1-IQ2_XS3.58 GiB3,840,261,5682.521mradermacher
I1-IQ2_S3.74 GiB4,020,710,8482.639mradermacher
I1-IQ2_M4.01 GiB4,310,461,8882.829mradermacher
I1-Q2_K_S4.14 GiB4,448,611,6482.920mradermacher
Q2_K4.44 GiB4,768,222,2403.130mradermacher
I1-Q2_K4.44 GiB4,768,222,5283.130mradermacher
I1-IQ3_XXS4.46 GiB4,784,901,5683.141mradermacher
I1-IQ3_XS4.85 GiB5,206,166,8483.417mradermacher
Q3_K_S5.08 GiB5,458,316,3203.583mradermacher
I1-Q3_K_S5.08 GiB5,458,316,6083.583mradermacher
I1-IQ3_S5.08 GiB5,458,316,6083.583mradermacher
I1-IQ3_M5.27 GiB5,655,723,3283.712mradermacher
Q3_K_M5.60 GiB6,008,818,7203.944mradermacher
I1-Q3_K_M5.60 GiB6,008,819,0083.944mradermacher
Q3_K_L6.04 GiB6,480,186,4004.254mradermacher
I1-Q3_K_L6.04 GiB6,480,186,6884.254mradermacher
I1-IQ4_XS6.10 GiB6,550,965,5684.300mradermacher
IQ4_XS6.15 GiB6,606,261,2804.337mradermacher
I1-IQ4_NL6.41 GiB6,887,165,2484.521mradermacher
I1-Q4_06.43 GiB6,909,283,6484.535mradermacher
Q4_K_S6.46 GiB6,935,333,9204.553mradermacher
I1-Q4_K_S6.46 GiB6,935,334,2084.553mradermacher
Q4_K_M6.80 GiB7,300,779,0404.792mradermacher
I1-Q4_K_M6.80 GiB7,300,779,3284.792mradermacher
I1-Q4_17.04 GiB7,559,564,6084.962mradermacher
Q5_K_S7.67 GiB8,231,963,6805.404mradermacher
I1-Q5_K_S7.67 GiB8,231,963,9685.404mradermacher
Q5_K_M7.87 GiB8,445,037,1205.543Ryex
Q5_K_M7.87 GiB8,445,037,6005.543mradermacher
I1-Q5_K_M7.87 GiB8,445,037,8885.543mradermacher
Q6_K9.00 GiB9,660,812,3206.341mradermacher
I1-Q6_K9.00 GiB9,660,812,6086.341mradermacher
Q8_011.65 GiB12,510,213,2808.212mradermacher

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

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 6.38 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 Floppa-12B-Gemma3-Uncensored need?
Q4_K_M is exactly 7,300,779,040 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 Floppa-12B-Gemma3-Uncensored'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 Floppa-12B-Gemma3-Uncensored 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.