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Gemma3-4B-CodeCenturion

sillykiwi/Gemma3-4B-CodeCenturion

Gemma3-4B-CodeCenturion at Q4_K_M is exactly 2,489,761,152 bytes (2.32 GiB / 2.49 GB) — an effective 4.632 bits per weight, not the nominal 4. Its KV cache at 32K is 0.79 GiB, not the 4.25 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
4.3B
Architecture
gemma3
34 layers
Context
131,072
native (config.json)
License

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S1.06 GiB1,132,981,5682.108mradermacher
I1-IQ1_M1.12 GiB1,199,459,6482.232mradermacher
I1-IQ2_XXS1.22 GiB1,310,256,4482.438mradermacher
I1-IQ2_XS1.31 GiB1,404,464,4482.613mradermacher
I1-IQ2_S1.35 GiB1,449,233,7282.696mradermacher
I1-IQ2_M1.43 GiB1,537,871,1682.861mradermacher
I1-Q2_K_S1.52 GiB1,635,929,7603.044mradermacher
I1-IQ3_XXS1.57 GiB1,689,341,2483.143mradermacher
Q2_K1.61 GiB1,729,031,5523.217mradermacher
I1-Q2_K1.61 GiB1,729,031,8403.217mradermacher
I1-IQ3_XS1.74 GiB1,863,257,7603.467mradermacher
Q3_K_S1.80 GiB1,937,231,2323.604mradermacher
I1-IQ3_S1.80 GiB1,937,231,5203.604mradermacher
I1-Q3_K_S1.80 GiB1,937,231,5203.604mradermacher
I1-IQ3_M1.85 GiB1,986,670,2403.696mradermacher
Q3_K_M1.95 GiB2,098,326,9123.904mradermacher
I1-Q3_K_M1.95 GiB2,098,327,2003.904mradermacher
Q3_K_L2.08 GiB2,235,952,5124.160mradermacher
I1-Q3_K_L2.08 GiB2,235,952,8004.160mradermacher
I1-IQ4_XS2.11 GiB2,263,109,2804.210mradermacher
IQ4_XS2.12 GiB2,279,492,9924.241mradermacher
I1-IQ4_NL2.20 GiB2,363,379,3604.397mradermacher
I1-Q4_02.21 GiB2,369,932,9604.409mradermacher
Q4_K_S2.21 GiB2,377,796,9924.424mradermacher
I1-Q4_K_S2.21 GiB2,377,797,2804.424mradermacher
Q4_K_M2.32 GiB2,489,761,1524.632mradermacher
I1-Q4_K_M2.32 GiB2,489,761,4404.632mradermacher
I1-Q4_12.39 GiB2,563,919,5204.770mradermacher
Q5_K_S2.57 GiB2,764,459,3925.143mradermacher
I1-Q5_K_S2.57 GiB2,764,459,6805.143mradermacher
Q5_K_M2.64 GiB2,829,565,3125.264mradermacher
I1-Q5_K_M2.64 GiB2,829,565,6005.264mradermacher
Q6_K2.97 GiB3,190,607,2325.936mradermacher
I1-Q6_K2.97 GiB3,190,607,5205.936mradermacher
Q8_03.85 GiB4,130,230,2407.684mradermacher
F167.23 GiB7,767,480,64014.451mradermacher

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.25 GiB0.53 GiB2.14×5 / 29 / 0
8,1920.33 GiB1.06 GiB3.26×5 / 29 / 0
16,3840.48 GiB2.13 GiB4.40×5 / 29 / 0
32,7680.79 GiB4.25 GiB5.35×5 / 29 / 0
65,5361.42 GiB8.50 GiB5.99×5 / 29 / 0
131,0722.67 GiB17.00 GiB6.37×5 / 29 / 0

29 of 34 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 Q4_K_M at roughly 2.25 GiB. The real file is 2.32 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 4.25 GiB at 32K context where the real figure is 0.79 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json
Layers
34
Attention heads
8
KV heads
4
Head dim
256
Hidden size
2560
Vocab
262,145
Sliding window
1024
SWA period
6
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does Gemma3-4B-CodeCenturion need?
Q4_K_M is exactly 2,489,761,152 bytes (2.32 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Gemma3-4B-CodeCenturion's KV cache?
0.79 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 Gemma3-4B-CodeCenturion 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.