How much VRAM does Huihui-Qwen3-VL-4B-Instruct-abliterated need?

Q4_K_M is exactly 2,497,282,240 bytes (2.33 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is Huihui-Qwen3-VL-4B-Instruct-abliterated'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.

huihui-ai · vision language

Huihui-Qwen3-VL-4B-Instruct-abliterated

huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated

Huihui-Qwen3-VL-4B-Instruct-abliterated at Q4_K_M is exactly 2,497,282,240 bytes (2.33 GiB / 2.50 GB) — an effective 4.502 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

4.4B

Architecture

qwen3vl

36 layers

Context

262,144

native (config.json)

License

—

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
I1-IQ1_S	0.98 GiB	1,055,257,856	1.902	—	mradermacher
I1-IQ1_M	1.05 GiB	1,127,019,776	2.032	—	mradermacher
I1-IQ2_XXS	1.16 GiB	1,246,622,976	2.247	—	mradermacher
I1-IQ2_XS	1.26 GiB	1,354,102,016	2.441	—	mradermacher
I1-IQ2_S	1.32 GiB	1,417,303,296	2.555	—	mradermacher
I1-IQ2_M	1.41 GiB	1,512,985,856	2.727	—	mradermacher
I1-Q2_K_S	1.46 GiB	1,563,456,256	2.818	—	mradermacher
Q2_K	1.55 GiB	1,669,501,376	3.010	—	mradermacher
I1-Q2_K	1.55 GiB	1,669,501,696	3.010	—	mradermacher
IQ3_XXS	1.56 GiB	1,670,189,760	3.011	—	noctrex
I1-IQ3_XXS	1.56 GiB	1,670,190,336	3.011	—	mradermacher
IQ3_XS	1.69 GiB	1,814,376,640	3.271	—	noctrex
I1-IQ3_XS	1.69 GiB	1,814,377,216	3.271	—	mradermacher
Q3_K_S	1.76 GiB	1,886,998,976	3.402	—	mradermacher
I1-Q3_K_S	1.76 GiB	1,886,999,296	3.402	—	mradermacher
IQ3_S	1.77 GiB	1,899,532,480	3.424	—	noctrex
I1-IQ3_S	1.77 GiB	1,899,533,056	3.424	—	mradermacher
IQ3_M	1.83 GiB	1,962,897,600	3.538	—	noctrex
I1-IQ3_M	1.83 GiB	1,962,898,176	3.538	—	mradermacher
Q3_K_M	1.93 GiB	2,075,619,776	3.742	—	mradermacher
I1-Q3_K_M	1.93 GiB	2,075,620,096	3.742	—	mradermacher
Q3_K_L	2.09 GiB	2,239,787,456	4.038	—	mradermacher
I1-Q3_K_L	2.09 GiB	2,239,787,776	4.038	—	mradermacher
IQ4_XS	2.11 GiB	2,270,752,960	4.093	—	noctrex
I1-IQ4_XS	2.11 GiB	2,270,753,536	4.093	—	mradermacher
IQ4_XS	2.13 GiB	2,286,318,016	4.122	—	mradermacher
I1-Q4_0	2.21 GiB	2,375,774,976	4.283	—	mradermacher
IQ4_NL	2.22 GiB	2,381,344,960	4.293	—	noctrex
I1-IQ4_NL	2.22 GiB	2,381,345,536	4.293	—	mradermacher
Q4_K_S	2.22 GiB	2,383,311,040	4.296	—	noctrex
Q4_K_S	2.22 GiB	2,383,311,296	4.296	—	mradermacher
I1-Q4_K_S	2.22 GiB	2,383,311,616	4.296	—	mradermacher
Q4_K_M	2.33 GiB	2,497,282,240	4.502	—	noctrex
Q4_K_M	2.33 GiB	2,497,282,496	4.502	—	mradermacher
I1-Q4_K_M	2.33 GiB	2,497,282,816	4.502	—	mradermacher
I1-Q4_1	2.42 GiB	2,596,631,296	4.681	—	mradermacher
Q5_K_S	2.63 GiB	2,823,712,960	5.090	—	noctrex
Q5_K_S	2.63 GiB	2,823,713,216	5.090	—	mradermacher
I1-Q5_K_S	2.63 GiB	2,823,713,536	5.090	—	mradermacher
Q5_K_M	2.69 GiB	2,889,515,200	5.209	—	noctrex

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 Qwen3.5-4B vs Qwen3.5-0.8B vs gemma-4-E2B-it vs Qwen3-VL-4B-Instruct vs Qwen2.5-VL-7B-Instruct vs Qwen3-VL-2B-Instruct 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 2.32 GiB. The real file is 2.33 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

2560

Vocab

151,936

Sliding window

none

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

—

Questions people ask

How much VRAM does Huihui-Qwen3-VL-4B-Instruct-abliterated need?: Q4_K_M is exactly 2,497,282,240 bytes (2.33 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Huihui-Qwen3-VL-4B-Instruct-abliterated'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 Huihui-Qwen3-VL-4B-Instruct-abliterated 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.