Iloqt · text

Barcenas-StyleTune-31B-Fable

Iloqt/Barcenas-StyleTune-31B-Fable

Barcenas-StyleTune-31B-Fable at Q4_K_M is exactly 19,479,772,480 bytes (18.14 GiB / 19.48 GB) — an effective 4.854 bits per weight, not the nominal 4. Its KV cache at 32K is 6.17 GiB, not the 30.00 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
32.1B
Architecture
gemma4
60 layers
Context
262,144
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S7.10 GiB7,618,897,5041.898mradermacher
I1-IQ1_M7.63 GiB8,188,280,4162.040mradermacher
I1-IQ2_XXS8.51 GiB9,137,251,9362.277mradermacher
I1-IQ2_XS9.31 GiB9,992,767,0722.490mradermacher
I1-IQ2_S10.02 GiB10,763,427,4242.682mradermacher
I1-Q2_K_S10.65 GiB11,438,997,0882.850mradermacher
I1-IQ2_M10.73 GiB11,522,604,6402.871mradermacher
Q2_K11.53 GiB12,378,721,6003.084mradermacher
I1-Q2_K11.53 GiB12,378,721,8883.084mradermacher
I1-IQ3_XXS11.81 GiB12,683,046,4963.160mradermacher
I1-IQ3_XS12.74 GiB13,677,907,5523.408mradermacher
Q3_K_S13.38 GiB14,366,895,4243.580mradermacher
I1-Q3_K_S13.38 GiB14,366,895,7123.580mradermacher
I1-IQ3_S13.38 GiB14,366,895,7123.580mradermacher
I1-IQ3_M14.00 GiB15,030,036,0643.745mradermacher
Q3_K_M14.80 GiB15,892,647,2323.960mradermacher
I1-Q3_K_M14.80 GiB15,892,647,5203.960mradermacher
Q3_K_L16.05 GiB17,233,808,7044.294mradermacher
I1-Q3_K_L16.05 GiB17,233,808,9924.294mradermacher
I1-IQ4_XS16.28 GiB17,484,459,6164.357mradermacher
IQ4_XS16.40 GiB17,610,902,8484.388mradermacher
I1-Q4_017.22 GiB18,494,287,4564.608mradermacher
Q4_K_S17.28 GiB18,555,874,6244.624mradermacher
I1-Q4_K_S17.28 GiB18,555,874,9124.624mradermacher
Q4_K_M18.14 GiB19,479,772,4804.854mradermacher
I1-Q4_K_M18.14 GiB19,479,772,7684.854mradermacher
I1-Q4_118.96 GiB20,362,210,9125.074mradermacher
Q5_K_S20.75 GiB22,280,711,4885.552mradermacher
I1-Q5_K_S20.75 GiB22,280,711,7765.552mradermacher
Q5_K_M21.25 GiB22,814,440,7685.685mradermacher
I1-Q5_K_M21.25 GiB22,814,441,0565.685mradermacher
Q6_K24.55 GiB26,357,525,8246.567mradermacher
I1-Q6_K24.55 GiB26,357,526,1126.567mradermacher
Q8_031.79 GiB34,133,028,1608.505mradermacher

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0961.80 GiB3.75 GiB2.09×10 / 50 / 0
8,1922.42 GiB7.50 GiB3.10×10 / 50 / 0
16,3843.67 GiB15.00 GiB4.09×10 / 50 / 0
32,7686.17 GiB30.00 GiB4.86×10 / 50 / 0
65,53611.17 GiB60.00 GiB5.37×10 / 50 / 0
131,07221.17 GiB120.00 GiB5.67×10 / 50 / 0

50 of 60 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 16.82 GiB. The real file is 18.14 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 30.00 GiB at 32K context where the real figure is 6.17 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json
Layers
60
Attention heads
32
KV heads
16
Head dim
256
Hidden size
5376
Vocab
262,144
Sliding window
1024
SWA period
MLA
no
Experts
0
Experts per token
use_sliding_window

Questions people ask

How much VRAM does Barcenas-StyleTune-31B-Fable need?
Q4_K_M is exactly 19,479,772,480 bytes (18.14 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Barcenas-StyleTune-31B-Fable's KV cache?
6.17 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 Barcenas-StyleTune-31B-Fable 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.