SvenBrnn · text

gemma-4-31B-heretic-finetune

SvenBrnn/gemma-4-31B-heretic-finetune

gemma-4-31B-heretic-finetune at I1-IQ1_S is exactly 7,156,489,696 bytes (6.67 GiB / 7.16 GB) — an effective 1.865 bits per weight, not the nominal 1. 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
30.7B
Architecture
gemma4
60 layers
Context
262,144
native (config.json)
License

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S6.67 GiB7,156,489,6961.865mradermacher
I1-IQ1_M7.20 GiB7,725,872,6082.013mradermacher
I1-IQ2_XXS8.08 GiB8,674,844,1282.261mradermacher
I1-IQ2_XS8.88 GiB9,530,359,2642.484mradermacher
I1-IQ2_S9.46 GiB10,157,888,9922.647mradermacher
I1-IQ2_M10.17 GiB10,917,066,2082.845mradermacher
I1-Q2_K_S10.22 GiB10,976,589,2802.861mradermacher
I1-Q2_K11.10 GiB11,916,314,0803.106mradermacher
I1-IQ3_XXS11.25 GiB12,077,508,0643.147mradermacher
I1-IQ3_XS12.17 GiB13,072,369,1203.407mradermacher
I1-IQ3_S12.82 GiB13,761,357,2803.586mradermacher
I1-Q3_K_S12.82 GiB13,761,357,2803.586mradermacher
I1-IQ3_M13.43 GiB14,424,497,6323.759mradermacher
I1-Q3_K_M14.24 GiB15,287,109,0883.984mradermacher
I1-Q3_K_L15.49 GiB16,628,270,5604.333mradermacher
I1-IQ4_XS15.59 GiB16,735,790,5604.362mradermacher
I1-Q4_016.49 GiB17,701,578,2084.613mradermacher
I1-Q4_K_S16.54 GiB17,763,165,6644.629mradermacher
I1-Q4_K_M17.40 GiB18,687,063,5204.870mradermacher
I1-Q4_118.14 GiB19,481,421,2805.077mradermacher
I1-Q5_K_S19.85 GiB21,311,841,7605.554mradermacher
I1-Q5_K_M20.35 GiB21,845,571,0405.693mradermacher
I1-Q6_K23.47 GiB25,201,485,2806.568mradermacher

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 I1-IQ1_S at roughly 16.08 GiB. The real file is 6.67 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 gemma-4-31B-heretic-finetune need?
I1-IQ1_S is exactly 7,156,489,696 bytes (6.67 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-4-31B-heretic-finetune'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 gemma-4-31B-heretic-finetune 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.