mdamir97 · text

alduin-4b-it-base

mdamir97/alduin-4b-it-base

alduin-4b-it-base at I1-IQ1_S is exactly 1,141,296,512 bytes (1.06 GiB / 1.14 GB) — an effective 2.123 bits per weight, not the nominal 1. 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
gemma

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S1.06 GiB1,141,296,5122.123mradermacher
I1-IQ1_M1.12 GiB1,207,774,5922.247mradermacher
I1-IQ2_XXS1.23 GiB1,318,571,3922.453mradermacher
I1-IQ2_XS1.32 GiB1,412,779,3922.628mradermacher
I1-IQ2_S1.36 GiB1,457,548,6722.712mradermacher
I1-IQ2_M1.44 GiB1,546,186,1122.877mradermacher
I1-Q2_K_S1.53 GiB1,644,266,1123.059mradermacher
I1-IQ3_XXS1.58 GiB1,697,656,1923.158mradermacher
I1-Q2_K1.62 GiB1,737,368,1923.232mradermacher
I1-IQ3_XS1.74 GiB1,871,594,1123.482mradermacher
I1-Q3_K_S1.81 GiB1,945,567,8723.620mradermacher
I1-IQ3_S1.81 GiB1,945,567,8723.620mradermacher
I1-IQ3_M1.86 GiB1,995,006,5923.712mradermacher
I1-Q3_K_M1.96 GiB2,106,663,5523.919mradermacher
I1-Q3_K_L2.09 GiB2,244,289,1524.175mradermacher
I1-IQ4_XS2.12 GiB2,271,445,6324.226mradermacher
I1-IQ4_NL2.21 GiB2,371,715,7124.412mradermacher
I1-Q4_02.21 GiB2,378,269,3124.425mradermacher
I1-Q4_K_S2.22 GiB2,386,133,6324.439mradermacher
I1-Q4_K_M2.33 GiB2,498,097,7924.647mradermacher
I1-Q4_12.40 GiB2,572,255,8724.785mradermacher
I1-Q5_K_S2.58 GiB2,772,796,0325.159mradermacher
I1-Q5_K_M2.64 GiB2,837,901,9525.280mradermacher
I1-Q6_K2.98 GiB3,198,943,8725.951mradermacher

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 I1-IQ1_S at roughly 2.25 GiB. The real file is 1.06 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
262,208
Sliding window
1024
SWA period
6
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does alduin-4b-it-base need?
I1-IQ1_S is exactly 1,141,296,512 bytes (1.06 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is alduin-4b-it-base'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 alduin-4b-it-base 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.