Qwen · vision language · mixture of experts

Qwen3-VL-30B-A3B-Instruct

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

Qwen3-VL-30B-A3B-Instruct at Q4_K_M is exactly 18,556,687,168 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
QuantSizeExact bytesEffective bpwTensorsPublisher
UD-TQ1_07.60 GiB8,161,777,3762.102unsloth
UD-IQ1_S8.46 GiB9,088,194,2722.340unsloth
UD-IQ1_M9.00 GiB9,666,860,7682.489unsloth
UD-IQ2_XXS9.63 GiB10,341,816,0322.663unsloth
UD-IQ2_M10.10 GiB10,845,132,5122.792unsloth
Q2_K10.49 GiB11,258,611,4242.899unsloth
Q2_K_L10.55 GiB11,331,540,7042.918unsloth
UD-IQ3_XXS12.00 GiB12,888,086,2403.318unsloth
Q3_K_S12.38 GiB13,292,469,9843.422unsloth
Q3_K_M13.70 GiB14,711,848,6723.788579unsloth
IQ4_XS15.25 GiB16,378,074,8484.217579unsloth
IQ4_NL16.12 GiB17,310,783,2004.457unsloth
Q4_016.19 GiB17,379,989,2164.475579unsloth
Q4_K_S16.26 GiB17,456,010,9764.495unsloth
Q4_K_M17.28 GiB18,556,687,1684.778579Qwen
Q4_K_M17.28 GiB18,556,687,2004.778579lmstudio-community
Q4_K_M17.28 GiB18,556,688,0964.778unsloth
Q4_117.87 GiB19,192,501,9844.942unsloth
Q5_K_S19.63 GiB21,080,512,2245.428unsloth
Q5_K_M20.23 GiB21,725,583,0725.594579unsloth
Q6_K23.37 GiB25,092,533,0886.461579lmstudio-community
Q6_K23.37 GiB25,092,533,9846.461579unsloth
Q8_030.25 GiB32,483,932,9928.364579Qwen
Q8_030.25 GiB32,483,933,0248.364579lmstudio-community
Q8_030.25 GiB32,483,933,9208.364unsloth
BF162 shards56.90 GiB61,095,804,57615.731unsloth

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.38 GiB0.38 GiB48 / 0 / 0
8,1920.75 GiB0.75 GiB48 / 0 / 0
16,3841.50 GiB1.50 GiB48 / 0 / 0
32,7683.00 GiB3.00 GiB48 / 0 / 0
65,5366.00 GiB6.00 GiB48 / 0 / 0
131,07212.00 GiB12.00 GiB48 / 0 / 0

Compare with

same modality, comparable size

Will it run on your card?

full quant x context sweep

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-Instruct need?
Q4_K_M is exactly 18,556,687,168 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-Instruct'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-Instruct 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-Instruct 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.