FitMyLLM
Alibaba/Dense

AlibabaQwen3-VL 8B Instruct

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

chatvisionmultilingual
8.77B
Parameters
256K
Context length
19
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
7.5 GB
5.8 + 1.7 KV
good
Q5_K_S5.57
8.3 GB
6.6 + 1.7 KV
good
Q5_K_M5.7
8.4 GB
6.7 + 1.7 KV
good
Q6_K6.56
9.4 GB
7.7 + 1.7 KV
excellent
Q8_08.5
11.5 GB
9.8 + 1.7 KV
lossless
FP1616
19.7 GB
18.0 + 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 8B INSTRUCT NOW

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

Community Ratings

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Benchmarks (19)

IFEval76.4
MATH48.8
GPQA Diamond42.7
MMLU-PRO37.5
BBH36.6
LiveCodeBench33.2
IFBench32.3
τ²-Bench29.2
AIME27.3
AA Math27.3
MATH-50027.3
SciCode17.4
MUSR15.5
AA Long Context15.3
AA Intelligence14.3
GPQA8.9
AA Coding7.3
HLE2.9
Terminal-Bench2.3

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3-vl:8b-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 C2070
6 GB VRAM • 143 GB/s
NVIDIA
NVIDIA Tesla C2075
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla C2090
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla M2070
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla M2070-Q
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla M2075
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla M2090
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla X2070
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla X2090
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla K20X
6 GB VRAM • 250 GB/s
NVIDIA
NVIDIA Tesla K20Xm
6 GB VRAM • 250 GB/s
NVIDIA

Find the best GPU for Qwen3-VL 8B Instruct

Build Hardware for Qwen3-VL 8B Instruct
▸ SPEC SHEET

Qwen3-VL 8B Instruct8.77B Dense.

▸ SPECIFICATIONS
PARAMETERS
8.77B
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_M4.895.8 GB94%
Q5_K_S5.576.6 GB96%
Q5_K_M5.76.7 GB96%
Q6_K6.567.7 GB97%
Q8_08.59.8 GB100%
FP161618.0 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO37.5
MATH48.8
IFEval76.4
BBH36.6
GPQA8.9
MUSR15.5
GPQA Diamond42.7
LiveCodeBench33.2
AIME27.3
HLE2.9
AA Intelligence14.3
AA Coding7.3
AA Math27.3
aa_ifbench32.3
aa_terminal_bench2.3
aa_tau229.2
aa_scicode17.4
aa_lcr15.3
MATH-50027.3
§ 02RUN COMMAND

Run Qwen3-VL 8B Instruct locally with Ollama — needs 5.8 GB VRAM at Q4_K_M:

$ollama run qwen3-vl:8b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M