FitMyLLM
Alibaba/Mixture of Experts

AlibabaQwen3-VL 30B A3B Instruct

Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.

chatvisionmultilingualreasoning
31.07B
Parameters (3B active)
256K
Context length
20
Benchmarks
14
Quantizations
0
Architecture
MoE
Released
2025-10-15
Layers
48
KV Heads
4
Head Dim
128
Family
qwen

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
IQ3_XXS3.25
14.2 GB
13.1 + 1.1 KV
low
IQ3_XS3.5
15.2 GB
14.1 + 1.1 KV
low
Q3_K_S3.64
15.8 GB
14.6 + 1.1 KV
low
IQ3_M3.76
16.2 GB
15.1 + 1.1 KV
low
Q3_K_M4
17.1 GB
16.0 + 1.1 KV
low
Q3_K_L4.3
18.3 GB
17.2 + 1.1 KV
moderate
IQ4_XS4.46
18.9 GB
17.8 + 1.1 KV
moderate
Q4_K_S4.67
19.8 GB
18.6 + 1.1 KV
moderate
Q4_K_M4.89
20.6 GB
19.5 + 1.1 KV
good
Q5_K_S5.57
23.2 GB
22.1 + 1.1 KV
good
Q5_K_M5.7
23.8 GB
22.6 + 1.1 KV
good
Q6_K6.56
27.1 GB
26.0 + 1.1 KV
excellent
Q8_08.5
34.6 GB
33.5 + 1.1 KV
lossless
FP1616
63.8 GB
62.6 + 1.1 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN QWEN3-VL 30B A3B INSTRUCT NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (20)

IFEval78.9
AIME72.3
AA Math72.3
MATH-50072.3
GPQA Diamond69.5
MATH59.7
BBH58.3
MMLU-PRO52.9
LiveCodeBench47.6
IFBench33.1
BigCodeBench32.3
SciCode30.8
AA Long Context23.7
MUSR19.1
τ²-Bench19.0
AA Intelligence16.0
GPQA15.2
AA Coding14.3
HLE6.4
Terminal-Bench6.1

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3-vl:30b-a3b-instruct-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA RTX A4500
20 GB VRAM • 640 GB/s
NVIDIA
$2000
Apple M4 Pro (24GB)
24 GB VRAM • 273 GB/s
APPLE
$1399
NVIDIA L4 24GB
24 GB VRAM • 300 GB/s
NVIDIA
$2500
Apple M2 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M3 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M4 (24GB)
24 GB VRAM • 120 GB/s
APPLE
$699
NVIDIA Tesla M40 24 GB
24 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla P10
24 GB VRAM • 694 GB/s
NVIDIA
NVIDIA Tesla P40
24 GB VRAM • 347 GB/s
NVIDIA
NVIDIA RTX A5000
24 GB VRAM • 768 GB/s
NVIDIA
$2500
NVIDIA L40 CNX
24 GB VRAM • 864 GB/s
NVIDIA
$5000

Find the best GPU for Qwen3-VL 30B A3B Instruct

Build Hardware for Qwen3-VL 30B A3B Instruct
▸ SPEC SHEET

Qwen3-VL 30B A3B Instruct31.07B MoE.

▸ SPECIFICATIONS
PARAMETERS
31.07B (3B active)
ARCHITECTURE
Mixture of Experts
CONTEXT LENGTH
256K tokens
CAPABILITIES
chat, vision, multilingual, reasoning
RELEASE DATE
2025-10-15
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2513.1 GB82%
IQ3_XS3.514.1 GB84%
Q3_K_S3.6414.6 GB85%
IQ3_M3.7615.1 GB86%
Q3_K_M416.0 GB88%
Q3_K_L4.317.2 GB90%
IQ4_XS4.4617.8 GB92%
Q4_K_S4.6718.6 GB93%
Q4_K_M4.8919.5 GB94%
Q5_K_S5.5722.1 GB96%
Q5_K_M5.722.6 GB96%
Q6_K6.5626.0 GB97%
Q8_08.533.5 GB100%
FP161662.6 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO52.9
MATH59.7
IFEval78.9
BBH58.3
GPQA15.2
MUSR19.1
BigCodeBench32.3
GPQA Diamond69.5
LiveCodeBench47.6
AIME72.3
HLE6.4
AA Intelligence16.0
AA Coding14.3
AA Math72.3
aa_ifbench33.1
aa_terminal_bench6.1
aa_tau219.0
aa_scicode30.8
aa_lcr23.7
MATH-50072.3
§ 02RUN COMMAND

Run Qwen3-VL 30B A3B Instruct locally with Ollama — needs 19.5 GB VRAM at Q4_K_M:

$ollama run qwen3-vl:30b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M