How much VRAM does magnum-v2-12b need?

Q4_K_M is exactly 7,477,204,224 bytes (6.96 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is magnum-v2-12b's KV cache?

5.00 GiB at 32K context with an f16 cache, computed per layer from the model's architecture. Quantizing the cache to q8_0 roughly halves it.

anthracite-org · text

magnum-v2-12b

anthracite-org/magnum-v2-12b

magnum-v2-12b at Q4_K_M is exactly 7,477,204,224 bytes (6.96 GiB / 7.48 GB) — an effective 4.884 bits per weight, not the nominal 4. Its KV cache at 32K is 5.00 GiB.

From the file· summed from 1 file(s)From the file· KV per layer

Parameters

12.2B

Architecture

llama

40 layers

Context

1,024,000

native (config.json)

License

apache-2.0

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
I1-IQ1_S	2.79 GiB	2,999,211,680	1.959	—	mradermacher
I1-IQ1_M	3.00 GiB	3,221,624,480	2.104	—	mradermacher
I1-IQ2_XXS	3.35 GiB	3,592,312,480	2.346	—	mradermacher
I1-IQ2_XS	3.65 GiB	3,915,077,280	2.557	—	mradermacher
I1-IQ2_S	3.85 GiB	4,138,473,120	2.703	—	mradermacher
IQ2_M	4.13 GiB	4,435,023,104	2.897	—	bartowski
I1-IQ2_M	4.13 GiB	4,435,023,520	2.897	—	mradermacher
Q2_K	4.46 GiB	4,791,047,424	3.129	—	bartowski
I1-Q2_K	4.46 GiB	4,791,047,840	3.129	—	mradermacher
I1-IQ3_XXS	4.61 GiB	4,945,385,120	3.230	—	mradermacher
IQ3_XS	4.94 GiB	5,306,488,064	3.466	—	bartowski
I1-IQ3_XS	4.94 GiB	5,306,488,480	3.466	—	mradermacher
Q2_K_L	5.07 GiB	5,446,407,424	3.558	—	bartowski
Q3_K_S	5.15 GiB	5,534,225,664	3.615	—	bartowski
I1-Q3_K_S	5.15 GiB	5,534,226,080	3.615	—	mradermacher
I1-IQ3_S	5.18 GiB	5,562,078,880	3.633	—	mradermacher
IQ3_M	5.33 GiB	5,722,231,808	3.738	—	anthracite-org
IQ3_M	5.33 GiB	5,722,232,064	3.738	—	bartowski
I1-IQ3_M	5.33 GiB	5,722,232,480	3.738	—	mradermacher
Q3_K_M	5.67 GiB	6,083,089,664	3.973	—	bartowski
I1-Q3_K_M	5.67 GiB	6,083,090,080	3.973	—	mradermacher
Q3_K_L	6.11 GiB	6,561,502,464	4.286	—	bartowski
I1-Q3_K_L	6.11 GiB	6,561,502,880	4.286	—	mradermacher
IQ4_XS	6.28 GiB	6,742,709,504	4.404	—	bartowski
I1-IQ4_XS	6.28 GiB	6,742,709,920	4.404	—	mradermacher
I1-Q4_0	6.61 GiB	7,094,638,240	4.634	—	mradermacher
Q4_K_S	6.63 GiB	7,120,196,864	4.651	—	bartowski
I1-Q4_K_S	6.63 GiB	7,120,197,280	4.651	—	mradermacher
Q4_K	6.96 GiB	7,477,203,968	4.884	—	anthracite-org
Q4_K_M	6.96 GiB	7,477,204,224	4.884	—	bartowski
I1-Q4_K_M	6.96 GiB	7,477,204,640	4.884	—	mradermacher
Q4_K_L	7.43 GiB	7,975,277,824	5.209	—	bartowski
Q5_K_S	7.93 GiB	8,518,735,104	5.564	—	bartowski
I1-Q5_K_S	7.93 GiB	8,518,735,520	5.564	—	mradermacher
Q5_K	8.13 GiB	8,727,630,848	5.701	—	anthracite-org
Q5_K_M	8.13 GiB	8,727,631,104	5.701	—	bartowski
I1-Q5_K_M	8.13 GiB	8,727,631,520	5.701	—	mradermacher
Q5_K_L	8.51 GiB	9,141,818,624	5.971	—	bartowski
Q6_K	9.37 GiB	10,056,209,408	6.569	—	anthracite-org
Q6_K	9.37 GiB	10,056,209,664	6.569	—	bartowski

KV cache by context

computed per layer

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	0.63 GiB	0.63 GiB	—	40 / 0 / 0
8,192	1.25 GiB	1.25 GiB	—	40 / 0 / 0
16,384	2.50 GiB	2.50 GiB	—	40 / 0 / 0
32,768	5.00 GiB	5.00 GiB	—	40 / 0 / 0
65,536	10.00 GiB	10.00 GiB	—	40 / 0 / 0
131,072	20.00 GiB	20.00 GiB	—	40 / 0 / 0

Compare with

same modality, comparable size

vs gemma-4-12B-it-qat-q4_0-unquantized vs gemma-4-E4B-it vs Qwen3-4B vs Qwen3-8B vs Llama-3.2-1B-Instruct vs llama-3-youko-8b All alternatives →Will it fit on my card? →

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 6.42 GiB. The real file is 6.96 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json

Layers

Attention heads

KV heads

Head dim

128

Hidden size

5120

Vocab

131,072

Sliding window

none

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

—

Questions people ask

How much VRAM does magnum-v2-12b need?: Q4_K_M is exactly 7,477,204,224 bytes (6.96 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is magnum-v2-12b's KV cache?: 5.00 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 magnum-v2-12b 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.