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

Q4_K_M is exactly 1,107,410,528 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-Embedding-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 · vision language

Qwen3-VL-Embedding-2B

Qwen/Qwen3-VL-Embedding-2B

Qwen3-VL-Embedding-2B at Q4_K_M is exactly 1,107,410,528 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

—

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q2_K	0.72 GiB	777,797,216	2.925	—	DevQuasar
Q3_K_S	0.81 GiB	867,253,856	3.261	—	DevQuasar
Q3_K_M	0.88 GiB	939,540,064	3.533	—	DevQuasar
Q3_K_L	0.93 GiB	1,003,503,200	3.773	—	DevQuasar
Q4_K_S	0.99 GiB	1,060,191,840	3.987	—	DevQuasar
Q4_K_M	1.03 GiB	1,107,410,528	4.164	—	DevQuasar
Q5_K_S	1.15 GiB	1,230,585,440	4.627	—	DevQuasar
Q5_K_M	1.17 GiB	1,257,881,184	4.730	—	DevQuasar
Q6_K	1.32 GiB	1,417,756,256	5.331	—	DevQuasar
Q8_0	1.71 GiB	1,834,428,000	6.898	—	DevQuasar
F16	3.21 GiB	3,447,350,880	12.963	—	DevQuasar

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 Qwen3.5-4B vs Qwen3.5-0.8B vs Qwen3-VL-4B-Instruct vs Qwen3-VL-2B-Instruct vs Qwen3.5-2B vs LFM2.5-VL-1.6B 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-Embedding-2B need?: Q4_K_M is exactly 1,107,410,528 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-Embedding-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-Embedding-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.