McGill-NLP · vision language

AfriqueGemma-12B

McGill-NLP/AfriqueGemma-12B

AfriqueGemma-12B at I1-IQ1_S is exactly 3,277,796,736 bytes (3.05 GiB / 3.28 GB) — an effective 2.152 bits per weight, not the nominal 1. Its KV cache at 32K is 2.47 GiB, not the 12.00 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
12.2B
Architecture
gemma3
48 layers
Context
131,072
native (config.json)
License
cc-by-4.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S3.05 GiB3,277,796,7362.152mradermacher
I1-IQ1_M3.26 GiB3,495,110,0162.294mradermacher
I1-IQ2_XXS3.59 GiB3,857,298,8162.532mradermacher
I1-IQ2_XS3.88 GiB4,170,642,8162.738mradermacher
I1-IQ2_S4.15 GiB4,453,353,2162.923mradermacher
I1-IQ2_M4.42 GiB4,743,104,2563.114mradermacher
I1-Q2_K_S4.45 GiB4,778,992,8963.137mradermacher
I1-Q2_K4.75 GiB5,098,603,7763.347mradermacher
I1-IQ3_XXS4.86 GiB5,217,543,9363.425mradermacher
I1-IQ3_XS5.25 GiB5,638,809,2163.701mradermacher
I1-Q3_K_S5.49 GiB5,890,958,9763.867mradermacher
I1-IQ3_S5.49 GiB5,890,958,9763.867mradermacher
I1-IQ3_M5.67 GiB6,088,365,6963.997mradermacher
I1-Q3_K_M6.00 GiB6,441,461,3764.228mradermacher
I1-Q3_K_L6.44 GiB6,912,829,0564.538mradermacher
I1-IQ4_XS6.60 GiB7,085,869,0564.651mradermacher
I1-IQ4_NL6.94 GiB7,453,533,6964.893mradermacher
I1-Q4_06.96 GiB7,475,652,0964.907mradermacher
I1-Q4_K_S6.99 GiB7,501,702,6564.924mradermacher
I1-Q4_K_M7.33 GiB7,867,147,7765.164mradermacher
I1-Q4_17.63 GiB8,188,862,9765.375mradermacher
I1-Q5_K_S8.31 GiB8,924,192,2565.858mradermacher
I1-Q5_K_M8.51 GiB9,137,266,1765.998mradermacher
I1-Q6_K9.77 GiB10,486,766,9766.884mradermacher

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.72 GiB1.50 GiB2.09×8 / 40 / 0
8,1920.97 GiB3.00 GiB3.10×8 / 40 / 0
16,3841.47 GiB6.00 GiB4.09×8 / 40 / 0
32,7682.47 GiB12.00 GiB4.86×8 / 40 / 0
65,5364.47 GiB24.00 GiB5.37×8 / 40 / 0
131,0728.47 GiB48.00 GiB5.67×8 / 40 / 0

40 of 48 layers cache only a 1,024-token window rather than the full context, on a period of 6. 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 6.38 GiB. The real file is 3.05 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 12.00 GiB at 32K context where the real figure is 2.47 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json
Layers
48
Attention heads
16
KV heads
8
Head dim
256
Hidden size
3840
Vocab
262,208
Sliding window
1024
SWA period
6
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does AfriqueGemma-12B need?
I1-IQ1_S is exactly 3,277,796,736 bytes (3.05 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is AfriqueGemma-12B's KV cache?
2.47 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 AfriqueGemma-12B 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.