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Gemmasutra-Mini-2B-v1

TheDrummer/Gemmasutra-Mini-2B-v1

Gemmasutra-Mini-2B-v1 at Q4_K_M is exactly 1,708,582,720 bytes (1.59 GiB / 1.71 GB) — an effective 5.228 bits per weight, not the nominal 4. Its KV cache at 32K is 1.85 GiB, not the 3.25 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
2.6B
Architecture
gemma2
26 layers
Context
8,192
native (config.json)
License
other

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S0.78 GiB832,159,9682.546mradermacher
I1-IQ1_M0.81 GiB873,797,8562.674mradermacher
I1-IQ2_XXS0.88 GiB943,194,3362.886mradermacher
I1-IQ2_XS0.93 GiB1,002,545,3763.068mradermacher
I1-IQ2_S0.96 GiB1,032,497,3763.159mradermacher
I1-IQ2_M1.01 GiB1,088,014,5603.329mradermacher
I1-IQ3_XXS1.10 GiB1,181,685,9843.616mradermacher
I1-Q2_K1.15 GiB1,229,830,3683.763mradermacher
I1-IQ3_XS1.22 GiB1,314,212,0644.021mradermacher
I1-IQ3_S1.27 GiB1,360,660,7044.164mradermacher
I1-Q3_K_S1.27 GiB1,360,660,7044.164mradermacher
IQ3_M1.30 GiB1,393,561,4084.264bartowski
I1-IQ3_M1.30 GiB1,393,561,8244.264mradermacher
I1-Q3_K_M1.36 GiB1,461,668,0644.473mradermacher
Q3_K_L1.44 GiB1,550,436,1604.744bartowski
I1-Q3_K_L1.44 GiB1,550,436,5764.744mradermacher
IQ4_XS1.46 GiB1,566,250,8164.793bartowski
I1-IQ4_XS1.46 GiB1,566,251,2324.793mradermacher
I1-Q4_01.52 GiB1,633,491,1684.999mradermacher
Q4_K_S1.53 GiB1,638,651,7125.014bartowski
I1-Q4_K_S1.53 GiB1,638,652,1285.014mradermacher
Q4_K_M1.59 GiB1,708,582,7205.228bartowski
I1-Q4_K_M1.59 GiB1,708,583,1365.228mradermacher
Q4_K_L1.72 GiB1,851,430,7205.665bartowski
Q5_K_S1.75 GiB1,882,543,9365.761bartowski
I1-Q5_K_S1.75 GiB1,882,544,3525.761mradermacher
Q5_K_M1.79 GiB1,923,278,6565.885bartowski
I1-Q5_K_M1.79 GiB1,923,279,0725.885mradermacher
Q5_K_L1.92 GiB2,066,126,6566.322bartowski
Q6_K2.00 GiB2,151,393,0886.583bartowski
I1-Q6_K2.00 GiB2,151,393,5046.583mradermacher
Q6_K_L2.14 GiB2,294,241,0887.021bartowski
Q8_02.59 GiB2,784,495,4248.521bartowski
F329.74 GiB10,463,413,79232.019bartowski

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.41 GiB0.41 GiB13 / 13 / 0
8,1920.63 GiB0.81 GiB1.28×13 / 13 / 0
16,3841.04 GiB1.63 GiB1.56×13 / 13 / 0
32,7681.85 GiB3.25 GiB1.75×13 / 13 / 0
65,5363.48 GiB6.50 GiB1.87×13 / 13 / 0
131,0726.73 GiB13.00 GiB1.93×13 / 13 / 0

13 of 26 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 1.37 GiB. The real file is 1.59 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 3.25 GiB at 32K context where the real figure is 1.85 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json
Layers
26
Attention heads
8
KV heads
4
Head dim
256
Hidden size
2304
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 Gemmasutra-Mini-2B-v1 need?
Q4_K_M is exactly 1,708,582,720 bytes (1.59 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Gemmasutra-Mini-2B-v1's KV cache?
1.85 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 Gemmasutra-Mini-2B-v1 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.