FitMyLLM
Alibaba/Dense

AlibabaQwen3-VL 4B Instruct

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

chatvisionmultilingual
4.44B
Parameters
256K
Context length
17
Benchmarks
6
Quantizations
0
Architecture
Dense
Released
2025-10-15
Layers
36
KV Heads
8
Head Dim
128
Family
qwen

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q4_K_M4.89
4.9 GB
3.2 + 1.7 KV
good
Q5_K_S5.57
5.3 GB
3.6 + 1.7 KV
good
Q5_K_M5.7
5.3 GB
3.7 + 1.7 KV
good
Q6_K6.56
5.8 GB
4.1 + 1.7 KV
excellent
Q8_08.5
6.9 GB
5.2 + 1.7 KV
lossless
FP1616
11.1 GB
9.4 + 1.7 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 4B INSTRUCT NOW

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

Community Ratings

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Benchmarks (17)

IFEval75.6
MATH49.6
GPQA Diamond37.1
AIME37.0
AA Math37.0
MMLU-PRO36.5
BBH34.9
IFBench31.8
LiveCodeBench29.0
τ²-Bench23.4
SciCode13.7
AA Long Context13.0
AA Intelligence9.6
MUSR8.7
GPQA6.4
AA Coding4.6
HLE3.7

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3-vl:4b-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 Tesla C1080
4 GB VRAM • 102 GB/s
NVIDIA
NVIDIA Tesla K10
4 GB VRAM • 160 GB/s
NVIDIA
NVIDIA Tesla M4
4 GB VRAM • 88 GB/s
NVIDIA
AMD Radeon Instinct MI8
4 GB VRAM • 512 GB/s
AMD
NVIDIA RTX A1000 Embedded
4 GB VRAM • 224 GB/s
NVIDIA
NVIDIA RTX A2000 Embedded
4 GB VRAM • 192 GB/s
NVIDIA

Find the best GPU for Qwen3-VL 4B Instruct

Build Hardware for Qwen3-VL 4B Instruct
▸ SPEC SHEET

Qwen3-VL 4B Instruct4.44B Dense.

▸ SPECIFICATIONS
PARAMETERS
4.44B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
256K tokens
CAPABILITIES
chat, vision, multilingual
RELEASE DATE
2025-10-15
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.893.2 GB94%
Q5_K_S5.573.6 GB96%
Q5_K_M5.73.7 GB96%
Q6_K6.564.1 GB97%
Q8_08.55.2 GB100%
FP16169.4 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO36.5
MATH49.6
IFEval75.6
BBH34.9
GPQA6.4
MUSR8.7
GPQA Diamond37.1
LiveCodeBench29.0
AIME37.0
HLE3.7
AA Intelligence9.6
AA Coding4.6
AA Math37.0
aa_ifbench31.8
aa_tau223.4
aa_scicode13.7
aa_lcr13.0
§ 02RUN COMMAND

Run Qwen3-VL 4B Instruct locally with Ollama — needs 3.2 GB VRAM at Q4_K_M:

$ollama run qwen3-vl:4b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M