How much VRAM does Qwen3-VL-Reranker-2B need?

Q4_K_M is exactly 1,107,410,784 bytes (1.03 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is Qwen3-VL-Reranker-2B's KV cache?

3.50 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.

Qwen · text

Qwen3-VL-Reranker-2B

Qwen/Qwen3-VL-Reranker-2B

Qwen3-VL-Reranker-2B at Q4_K_M is exactly 1,107,410,784 bytes (1.03 GiB / 1.11 GB) — an effective 4.164 bits per weight, not the nominal 4. Its KV cache at 32K is 3.50 GiB.

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

Parameters

2.1B

Architecture

qwen3vl

28 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	0.48 GiB	515,778,656	1.939	—	mradermacher
I1-IQ1_M	0.51 GiB	543,795,296	2.045	—	mradermacher
I1-IQ2_XXS	0.55 GiB	590,489,696	2.220	—	mradermacher
I1-IQ2_XS	0.59 GiB	631,515,232	2.375	—	mradermacher
I1-IQ2_S	0.61 GiB	657,827,936	2.474	—	mradermacher
I1-IQ2_M	0.65 GiB	695,183,456	2.614	—	mradermacher
I1-Q2_K_S	0.68 GiB	732,971,104	2.756	—	mradermacher
I1-IQ3_XXS	0.70 GiB	754,362,464	2.837	—	mradermacher
Q2_K	0.72 GiB	777,797,472	2.925	—	mradermacher
I1-Q2_K	0.72 GiB	777,797,728	2.925	—	mradermacher
I1-IQ3_XS	0.78 GiB	834,224,224	3.137	—	mradermacher
Q3_K_S	0.81 GiB	867,254,112	3.261	—	mradermacher
I1-Q3_K_S	0.81 GiB	867,254,368	3.261	—	mradermacher
I1-IQ3_S	0.81 GiB	867,254,368	3.261	—	mradermacher
I1-IQ3_M	0.83 GiB	895,664,224	3.368	—	mradermacher
Q3_K_M	0.88 GiB	939,540,320	3.533	—	mradermacher
I1-Q3_K_M	0.88 GiB	939,540,576	3.533	—	mradermacher
Q3_K_L	0.93 GiB	1,003,503,456	3.773	—	mradermacher
I1-Q3_K_L	0.93 GiB	1,003,503,712	3.773	—	mradermacher
I1-IQ4_XS	0.94 GiB	1,010,384,992	3.799	—	mradermacher
IQ4_XS	0.95 GiB	1,016,282,976	3.821	—	mradermacher
I1-IQ4_NL	0.98 GiB	1,054,425,184	3.965	—	mradermacher
I1-Q4_0	0.98 GiB	1,056,784,480	3.974	—	mradermacher
Q4_K_S	0.99 GiB	1,060,192,096	3.987	—	mradermacher
I1-Q4_K_S	0.99 GiB	1,060,192,352	3.987	—	mradermacher
Q4_K_M	1.03 GiB	1,107,410,784	4.164	—	mradermacher
I1-Q4_K_M	1.03 GiB	1,107,411,040	4.164	—	mradermacher
I1-Q4_1	1.06 GiB	1,142,505,568	4.296	—	mradermacher
Q5_K_S	1.15 GiB	1,230,585,696	4.627	—	mradermacher
I1-Q5_K_S	1.15 GiB	1,230,585,952	4.627	—	mradermacher
Q5_K_M	1.17 GiB	1,257,881,440	4.730	—	mradermacher
I1-Q5_K_M	1.17 GiB	1,257,881,696	4.730	—	mradermacher
Q6_K	1.32 GiB	1,417,756,512	5.331	—	mradermacher
I1-Q6_K	1.32 GiB	1,417,756,768	5.331	—	mradermacher
Q8_0	1.71 GiB	1,834,428,256	6.898	—	mradermacher
F16	3.21 GiB	3,447,351,136	12.963	—	mradermacher

KV cache by context

computed per layer

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	0.44 GiB	0.44 GiB	—	28 / 0 / 0
8,192	0.88 GiB	0.88 GiB	—	28 / 0 / 0
16,384	1.75 GiB	1.75 GiB	—	28 / 0 / 0
32,768	3.50 GiB	3.50 GiB	—	28 / 0 / 0
65,536	7.00 GiB	7.00 GiB	—	28 / 0 / 0
131,072	14.00 GiB	14.00 GiB	—	28 / 0 / 0

Compare with

same modality, comparable size

vs Qwen3-4B vs HyperCLOVAX-SEED-Text-Instruct-1.5B vs Llama-3.2-1B-Instruct vs ced-base vs gemma-3-1b-it vs Qwen3-1.7B 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 1.11 GiB. The real file is 1.03 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

2048

Vocab

151,936

Sliding window

none

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

—

Questions people ask

How much VRAM does Qwen3-VL-Reranker-2B need?: Q4_K_M is exactly 1,107,410,784 bytes (1.03 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Qwen3-VL-Reranker-2B's KV cache?: 3.50 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 Qwen3-VL-Reranker-2B 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.