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bella-bartender-v2

juiceb0xc0de/bella-bartender-v2

bella-bartender-v2 at Q4_K_M is exactly 5,761,059,584 bytes (5.37 GiB / 5.76 GB) — an effective 4.987 bits per weight, not the nominal 4. Its KV cache at 32K is 5.99 GiB, not the 10.50 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
9.2B
Architecture
gemma2
42 layers
Context
8,192
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S2.22 GiB2,378,566,6562.059mradermacher
I1-IQ1_M2.37 GiB2,545,953,7922.204mradermacher
I1-IQ2_XXS2.63 GiB2,824,932,3522.445mradermacher
I1-IQ2_XS2.86 GiB3,067,382,7842.655mradermacher
I1-IQ2_S2.99 GiB3,211,488,2562.780mradermacher
I1-IQ2_M3.20 GiB3,434,671,1042.973mradermacher
I1-Q2_K_S3.31 GiB3,552,513,0243.075mradermacher
I1-IQ3_XXS3.54 GiB3,796,741,1203.287mradermacher
Q2_K3.54 GiB3,805,399,8083.294mradermacher
I1-Q2_K3.54 GiB3,805,400,0643.294mradermacher
I1-IQ3_XS3.86 GiB4,144,991,2323.588mradermacher
Q3_K_S4.04 GiB4,337,666,8163.755mradermacher
I1-IQ3_S4.04 GiB4,337,667,0723.755mradermacher
I1-Q3_K_S4.04 GiB4,337,667,0723.755mradermacher
I1-IQ3_M4.19 GiB4,494,617,6003.891mradermacher
Q3_K_M4.43 GiB4,761,783,0404.122mradermacher
I1-Q3_K_M4.43 GiB4,761,783,2964.122mradermacher
Q3_K_L4.78 GiB5,132,454,6564.443mradermacher
I1-Q3_K_L4.78 GiB5,132,454,9124.443mradermacher
I1-IQ4_XS4.83 GiB5,183,032,3204.487mradermacher
IQ4_XS4.86 GiB5,223,172,8644.521mradermacher
I1-IQ4_NL5.07 GiB5,443,144,7044.712mradermacher
I1-Q4_05.08 GiB5,459,201,0244.726mradermacher
Q4_K_S5.10 GiB5,478,927,1044.743mradermacher
I1-Q4_K_S5.10 GiB5,478,927,3604.743mradermacher
Q4_K_M5.37 GiB5,761,059,5844.987mradermacher
I1-Q4_K_M5.37 GiB5,761,059,8404.987mradermacher
I1-Q4_15.55 GiB5,963,369,4725.162mradermacher
Q5_K_S6.04 GiB6,483,593,9845.612mradermacher
I1-Q5_K_S6.04 GiB6,483,594,2405.612mradermacher
Q5_K_M6.19 GiB6,647,368,4485.754mradermacher
I1-Q5_K_M6.19 GiB6,647,368,7045.754mradermacher
Q6_K7.07 GiB7,589,071,6166.569mradermacher
I1-Q6_K7.07 GiB7,589,071,8726.569mradermacher
Q8_09.15 GiB9,827,150,5928.507mradermacher
F1617.22 GiB18,490,682,11216.006mradermacher

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0961.31 GiB1.31 GiB21 / 21 / 0
8,1922.05 GiB2.63 GiB1.28×21 / 21 / 0
16,3843.36 GiB5.25 GiB1.56×21 / 21 / 0
32,7685.99 GiB10.50 GiB1.75×21 / 21 / 0
65,53611.24 GiB21.00 GiB1.87×21 / 21 / 0
131,07221.74 GiB42.00 GiB1.93×21 / 21 / 0

21 of 42 layers cache only a 4,096-token window rather than the full context, on a period of 2. 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 4.84 GiB. The real file is 5.37 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 10.50 GiB at 32K context where the real figure is 5.99 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json
Layers
42
Attention heads
16
KV heads
8
Head dim
256
Hidden size
3584
Vocab
256,000
Sliding window
4096
SWA period
2
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does bella-bartender-v2 need?
Q4_K_M is exactly 5,761,059,584 bytes (5.37 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is bella-bartender-v2's KV cache?
5.99 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 bella-bartender-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.