Qwen · vision language · mixture of experts

Qwen3-VL-30B-A3B-Thinking

Qwen/Qwen3-VL-30B-A3B-Thinking

Qwen3-VL-30B-A3B-Thinking at Q4_K_M is exactly 18,556,687,200 bytes (17.28 GiB / 18.56 GB) — an effective 4.778 bits per weight, not the nominal 4. Its KV cache at 32K is 3.00 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
31.1B
total, not active
Architecture
qwen3vlmoe
48 layers
Context
262,144
native (config.json)
License
apache-2.0

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
IQ2_XXS7.05 GiB7,569,026,1761.949—bartowski
UD-TQ1_07.59 GiB8,149,194,3682.098—unsloth
IQ2_XS8.07 GiB8,662,166,6562.230—bartowski
IQ2_S8.14 GiB8,744,100,9922.251—bartowski
UD-IQ1_S8.44 GiB9,066,174,0802.334—unsloth
UD-IQ1_M9.00 GiB9,666,860,6722.489—unsloth
IQ2_M9.19 GiB9,870,271,6162.541—bartowski
UD-IQ2_XXS9.63 GiB10,341,815,9362.663—unsloth
UD-IQ2_M10.10 GiB10,845,132,4162.792—unsloth
Q2_K10.16 GiB10,908,648,5762.809—bartowski
Q2_K_L10.44 GiB11,212,520,5762.887—bartowski
Q2_K10.49 GiB11,258,611,3282.899—unsloth
Q2_K_L10.55 GiB11,331,540,6082.918—unsloth
IQ3_XXS11.38 GiB12,216,984,7043.146—bartowski
IQ3_XS11.86 GiB12,736,461,9523.279—bartowski
UD-IQ3_XXS12.00 GiB12,888,086,1443.318—unsloth
Q3_K_S12.38 GiB13,292,469,8883.422—unsloth
Q3_K_S12.51 GiB13,428,128,8963.457—bartowski
IQ3_M13.11 GiB14,076,542,0803.624—bartowski
Q3_K_M13.11 GiB14,076,804,2243.624—bartowski
Q3_K_L13.58 GiB14,583,004,2883.755—bartowski
Q3_K_M13.70 GiB14,711,848,5763.788—unsloth
IQ4_XS15.25 GiB16,378,074,7524.217—unsloth
IQ4_XS15.33 GiB16,458,003,5844.238—bartowski
IQ4_NL16.12 GiB17,310,783,1044.457—unsloth
Q4_016.19 GiB17,379,989,5684.475—unsloth
IQ4_NL16.19 GiB17,386,280,0644.477—bartowski
Q4_K_S16.26 GiB17,456,010,8804.495—unsloth
Q4_016.42 GiB17,631,646,8484.540—bartowski
Q4_K_S16.75 GiB17,984,492,6724.631—bartowski
Q4_K_M17.28 GiB18,556,687,2004.778—Qwen
Q4_K_M17.28 GiB18,556,688,0004.778—unsloth
Q4_K_M17.35 GiB18,632,184,9604.797—bartowski
Q4_K_L17.57 GiB18,863,127,6804.857—bartowski
Q4_117.87 GiB19,192,501,8884.942—unsloth
Q4_117.89 GiB19,214,521,4724.947—bartowski
Q5_K_S19.63 GiB21,080,512,1285.428—unsloth
Q5_K_S19.65 GiB21,099,385,9845.433—bartowski
Q5_K_M20.23 GiB21,725,582,9765.594—unsloth
Q5_K_M20.25 GiB21,744,456,8325.599—bartowski

KV cache by context

computed per layer
ContextKV cache (f16)●Flat formulaOverstated byFull / windowed / recurrent
4,0960.38 GiB0.38 GiB—48 / 0 / 0
8,1920.75 GiB0.75 GiB—48 / 0 / 0
16,3841.50 GiB1.50 GiB—48 / 0 / 0
32,7683.00 GiB3.00 GiB—48 / 0 / 0
65,5366.00 GiB6.00 GiB—48 / 0 / 0
131,07212.00 GiB12.00 GiB—48 / 0 / 0

Compare with

same modality, comparable size
vs Qwen3.6-35B-A3Bvs Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-MTPvs Qwen3.5-9Bvs gemma-4-26B-A4B-itvs gemma-4-12B-itvs Qwen3.5-4BAll alternatives →Will it fit on my card? →

Will it run on your card?

full quant x context sweep
By VRAM:8GB12GB16GB24GB32GB48GB
Radeon RX 6500 XT 4GBGeForce RTX 3050 6GBGeForce RTX 5050 8GBGeForce RTX 3080 10GBGeForce RTX 2080 Ti 11GBGeForce RTX 5070 12GBGeForce RTX 5060 Ti 16GBApple M3 Pro 18GBGeForce RTX 3080 Ti 20GBGeForce RTX 5090 D V2 24GBGeForce RTX 5090 D 32GBApple M5 Max 36GBApple M5 Pro 48GBApple M5 Max 64GBApple M3 Ultra 96GBApple M5 Max 128GBApple M2 Ultra 192GBApple M3 Ultra 256GBApple M3 Ultra 512GB

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 16.28 GiB. The real file is 17.28 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
48
Attention heads
32
KV heads
4
Head dim
128
Hidden size
2048
Vocab
151,936
Sliding window
none
SWA period
—
MLA
no
Experts
128
Experts per token
8
use_sliding_window
—

Questions people ask

How much VRAM does Qwen3-VL-30B-A3B-Thinking need?
Q4_K_M is exactly 18,556,687,200 bytes (17.28 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-30B-A3B-Thinking's KV cache?
3.00 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.
Is Qwen3-VL-30B-A3B-Thinking a mixture-of-experts model?
Yes — 128 experts, 8 routed per token. Every expert must be resident, but only the routed ones are read per token, which is why its memory requirement and its speed behave very differently.
Which quantization of Qwen3-VL-30B-A3B-Thinking 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.