interpolators · text

gemma-4-12B-coder-fable5-composer2.5-v1-bf16

interpolators/gemma-4-12B-coder-fable5-composer2.5-v1-bf16

gemma-4-12B-coder-fable5-composer2.5-v1-bf16 at I1-IQ1_S is exactly 2,983,692,128 bytes (2.78 GiB / 2.98 GB) — an effective 1.996 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
12.0B
Architecture
gemma4
48 layers
Context
262,144
native (config.json)
License
gemma

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S2.78 GiB2,983,692,1281.996mradermacher
I1-IQ1_M2.98 GiB3,202,848,6082.142mradermacher
I1-IQ2_XXS3.32 GiB3,568,109,4082.387mradermacher
I1-IQ2_XS3.62 GiB3,887,843,1682.601mradermacher
I1-IQ2_S3.80 GiB4,079,597,4082.729mradermacher
I1-IQ2_M4.07 GiB4,371,806,0482.924mradermacher
I1-Q2_K_S4.19 GiB4,504,147,8083.013mradermacher
I1-Q2_K4.50 GiB4,830,148,4483.231mradermacher
I1-IQ3_XXS4.52 GiB4,849,194,8483.244mradermacher
I1-IQ3_XS4.91 GiB5,272,393,5683.527mradermacher
I1-Q3_K_S5.15 GiB5,528,229,7283.698mradermacher
I1-IQ3_S5.15 GiB5,528,229,7283.698mradermacher
I1-IQ3_M5.34 GiB5,733,992,2883.836mradermacher
I1-Q3_K_M5.67 GiB6,087,087,9684.072mradermacher
I1-Q3_K_L6.12 GiB6,566,319,9684.392mradermacher
I1-IQ4_XS6.18 GiB6,635,255,6484.438mradermacher
I1-IQ4_NL6.50 GiB6,975,879,0084.666mradermacher
I1-Q4_06.52 GiB6,997,997,4084.681mradermacher
I1-Q4_K_S6.54 GiB7,024,047,9684.699mradermacher
I1-Q4_K_M6.87 GiB7,381,383,0084.938mradermacher
I1-Q4_17.13 GiB7,657,125,7285.122mradermacher
I1-Q5_K_S7.77 GiB8,338,372,4485.578mradermacher
I1-Q5_K_M7.96 GiB8,547,268,4485.717mradermacher
I1-Q6_K9.11 GiB9,786,021,7286.546mradermacher

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 . 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 6.27 GiB. The real file is 2.78 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
262,144
Sliding window
1024
SWA period
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does gemma-4-12B-coder-fable5-composer2.5-v1-bf16 need?
I1-IQ1_S is exactly 2,983,692,128 bytes (2.78 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-4-12B-coder-fable5-composer2.5-v1-bf16'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 gemma-4-12B-coder-fable5-composer2.5-v1-bf16 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.