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Omni-31B-Turkish-Reasoning-Model

bugrabilge/Omni-31B-Turkish-Reasoning-Model

Omni-31B-Turkish-Reasoning-Model at Q4_K_M is exactly 18,687,046,720 bytes (17.40 GiB / 18.69 GB) — an effective 4.780 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
31.3B
Architecture
gemma4
60 layers
Context
262,144
native (config.json)
License
gemma

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S6.66 GiB7,156,473,1841.831mradermacher
I1-IQ1_M7.20 GiB7,725,856,0961.976mradermacher
I1-IQ2_XXS8.08 GiB8,674,827,6162.219mradermacher
I1-IQ2_XS8.88 GiB9,530,342,7522.438mradermacher
I1-IQ2_S9.46 GiB10,157,872,4802.599mradermacher
I1-IQ2_M10.17 GiB10,917,049,6962.793mradermacher
I1-Q2_K_S10.22 GiB10,976,572,7682.808mradermacher
Q2_K11.10 GiB11,916,297,2803.048mradermacher
I1-Q2_K11.10 GiB11,916,297,5683.048mradermacher
I1-IQ3_XXS11.25 GiB12,077,491,5523.090mradermacher
I1-IQ3_XS12.17 GiB13,072,352,6083.344mradermacher
Q3_K_S12.82 GiB13,761,340,4803.520mradermacher
I1-Q3_K_S12.82 GiB13,761,340,7683.520mradermacher
I1-IQ3_S12.82 GiB13,761,340,7683.520mradermacher
I1-IQ3_M13.43 GiB14,424,481,1203.690mradermacher
Q3_K_M14.24 GiB15,287,092,2883.911mradermacher
I1-Q3_K_M14.24 GiB15,287,092,5763.911mradermacher
Q3_K_L15.49 GiB16,628,253,7604.254mradermacher
I1-Q3_K_L15.49 GiB16,628,254,0484.254mradermacher
I1-IQ4_XS15.59 GiB16,735,774,0484.281mradermacher
IQ4_XS15.70 GiB16,862,217,2804.314mradermacher
I1-Q4_016.49 GiB17,701,561,6964.528mradermacher
Q4_K_S16.54 GiB17,763,148,8644.544mradermacher
I1-Q4_K_S16.54 GiB17,763,149,1524.544mradermacher
Q4_K_M17.40 GiB18,687,046,7204.780mradermacher
I1-Q4_K_M17.40 GiB18,687,047,0084.780mradermacher
I1-Q4_118.14 GiB19,481,404,7684.984mradermacher
Q5_K_S19.85 GiB21,311,824,9605.452mradermacher
I1-Q5_K_S19.85 GiB21,311,825,2485.452mradermacher
Q5_K_M20.35 GiB21,845,554,2405.588mradermacher
I1-Q5_K_M20.35 GiB21,845,554,5285.588mradermacher
Q6_K23.47 GiB25,201,468,4806.447mradermacher
I1-Q6_K23.47 GiB25,201,468,7686.447mradermacher
Q8_030.39 GiB32,635,659,3288.349mradermacher

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 16.38 GiB. The real file is 17.40 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 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
Experts per token
use_sliding_window

Questions people ask

How much VRAM does Omni-31B-Turkish-Reasoning-Model need?
Q4_K_M is exactly 18,687,046,720 bytes (17.40 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Omni-31B-Turkish-Reasoning-Model'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 Omni-31B-Turkish-Reasoning-Model 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.