How much VRAM does Jan-v2-VL-med need?

Q4_K_M is exactly 5,027,784,672 bytes (4.68 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is Jan-v2-VL-med's KV cache?

4.50 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.

janhq · vision language

Jan-v2-VL-med

janhq/Jan-v2-VL-med

Jan-v2-VL-med at Q4_K_M is exactly 5,027,784,672 bytes (4.68 GiB / 5.03 GB) — an effective 4.588 bits per weight, not the nominal 4. Its KV cache at 32K is 4.50 GiB.

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

Parameters

8.8B

Architecture

qwen3vl

36 layers

Context

262,144

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	1.97 GiB	2,115,771,360	1.931	—	mradermacher
I1-IQ1_M	2.10 GiB	2,256,149,472	2.059	—	mradermacher
I1-IQ2_XXS	2.32 GiB	2,490,112,992	2.272	—	mradermacher
I1-IQ2_XS	2.51 GiB	2,696,158,176	2.460	—	mradermacher
I1-IQ2_S	2.67 GiB	2,864,745,440	2.614	—	mradermacher
I1-IQ2_M	2.84 GiB	3,051,916,256	2.785	—	mradermacher
I1-Q2_K_S	2.87 GiB	3,083,553,760	2.814	—	mradermacher
Q2_K	3.06 GiB	3,281,734,368	2.995	—	mradermacher
I1-Q2_K	3.06 GiB	3,281,734,624	2.995	—	mradermacher
I1-IQ3_XXS	3.14 GiB	3,369,634,784	3.075	—	mradermacher
I1-IQ3_XS	3.38 GiB	3,626,875,872	3.309	—	mradermacher
Q3_K_S	3.51 GiB	3,769,612,256	3.440	—	janhq
Q3_K_S	3.51 GiB	3,769,613,024	3.440	—	mradermacher
I1-Q3_K_S	3.51 GiB	3,769,613,280	3.440	—	mradermacher
I1-IQ3_S	3.53 GiB	3,789,667,296	3.458	—	mradermacher
I1-IQ3_M	3.63 GiB	3,896,622,048	3.556	—	mradermacher
Q3_K_M	3.84 GiB	4,124,162,016	3.763	—	janhq
Q3_K_M	3.84 GiB	4,124,162,784	3.763	—	mradermacher
I1-Q3_K_M	3.84 GiB	4,124,163,040	3.763	—	mradermacher
Q3_K_L	4.13 GiB	4,431,394,784	4.044	—	janhq
Q3_K_L	4.13 GiB	4,431,395,552	4.044	—	mradermacher
I1-Q3_K_L	4.13 GiB	4,431,395,808	4.044	—	mradermacher
I1-IQ4_XS	4.25 GiB	4,561,841,120	4.163	—	mradermacher
IQ4_XS	4.28 GiB	4,593,298,144	4.191	—	mradermacher
Q4_0	4.45 GiB	4,774,750,176	4.357	—	janhq
I1-Q4_0	4.46 GiB	4,787,334,112	4.368	—	mradermacher
I1-IQ4_NL	4.46 GiB	4,793,625,568	4.374	—	mradermacher
Q4_K_S	4.47 GiB	4,802,013,152	4.382	—	janhq
Q4_K_S	4.47 GiB	4,802,013,920	4.382	—	mradermacher
I1-Q4_K_S	4.47 GiB	4,802,014,176	4.382	—	mradermacher
Q4_K_M	4.68 GiB	5,027,784,672	4.588	—	janhq
Q4_K_M	4.68 GiB	5,027,785,440	4.588	—	mradermacher
I1-Q4_K_M	4.68 GiB	5,027,785,696	4.588	—	mradermacher
Q4_1	4.89 GiB	5,247,756,256	4.789	—	janhq
I1-Q4_1	4.89 GiB	5,247,757,280	4.789	—	mradermacher
Q5_0	5.33 GiB	5,720,762,336	5.220	—	janhq
Q5_K_S	5.33 GiB	5,720,762,336	5.220	—	janhq
Q5_K_S	5.33 GiB	5,720,763,104	5.220	—	mradermacher
I1-Q5_K_S	5.33 GiB	5,720,763,360	5.220	—	mradermacher
Q5_K_M	5.45 GiB	5,851,113,440	5.339	—	janhq

KV cache by context

computed per layer

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	0.56 GiB	0.56 GiB	—	36 / 0 / 0
8,192	1.13 GiB	1.13 GiB	—	36 / 0 / 0
16,384	2.25 GiB	2.25 GiB	—	36 / 0 / 0
32,768	4.50 GiB	4.50 GiB	—	36 / 0 / 0
65,536	9.00 GiB	9.00 GiB	—	36 / 0 / 0
131,072	18.00 GiB	18.00 GiB	—	36 / 0 / 0

Compare with

same modality, comparable size

vs Qwen3.5-9B vs gemma-4-12B-it vs Qwen3.5-4B vs Qwen3.5-0.8B vs gemma-4-E2B-it vs gemma-4-E4B-it-qat-q4_0-unquantized 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 4.59 GiB. The real file is 4.68 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

4096

Vocab

151,936

Sliding window

none

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

—

Questions people ask

How much VRAM does Jan-v2-VL-med need?: Q4_K_M is exactly 5,027,784,672 bytes (4.68 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Jan-v2-VL-med's KV cache?: 4.50 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 Jan-v2-VL-med 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.