FitMyLLM
Alibaba/Dense

AlibabaQwen 3.5 0.8B

Qwen 3.5 0.8B — tiny multimodal model for edge devices. Vision, 262K context, 201 languages.

chatcodingmultilingualvision
0.87B
Parameters
256K
Context length
17
Benchmarks
6
Quantizations
2.0M
HF downloads
Architecture
Dense
Released
2026-03-01
Layers
24
KV Heads
2
Head Dim
256
Family
qwen

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q4_K_M4.89
1.2 GB
1.0 + 0.1 KV
good
Q5_K_S5.57
1.2 GB
1.1 + 0.1 KV
good
Q5_K_M5.7
1.2 GB
1.1 + 0.1 KV
good
Q6_K6.56
1.3 GB
1.2 + 0.1 KV
excellent
Q8_08.5
1.6 GB
1.4 + 0.1 KV
lossless
FP1616
2.4 GB
2.2 + 0.1 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 QWEN 3.5 0.8B NOW

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

Community Ratings

Loading ratings...

Benchmarks (17)

MMBench69.9
τ²-Bench65.2
IFEval52.1
MMMU49.0
MMLU-PRO29.7
GPQA Diamond23.6
IFBench21.6
BBH16.3
MATH10.2
AA Intelligence9.9
BigCodeBench8.8
GPQA6.7
AA Long Context6.7
HLE4.9
MUSR4.6
SciCode2.9
AA Coding1.0

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3.5:0.8b

Tag may need adjustment — check ollama.com/library/qwen3.5 for available tags.

▸ 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 C870
2 GB VRAM • 76.8 GB/s
NVIDIA
NVIDIA Tesla D870
2 GB VRAM • 76.8 GB/s
NVIDIA
NVIDIA Tesla S870
2 GB VRAM • 76.8 GB/s
NVIDIA

Find the best GPU for Qwen 3.5 0.8B

Build Hardware for Qwen 3.5 0.8B
▸ SPEC SHEET

Qwen 3.5 0.8B0.87B Dense.

▸ SPECIFICATIONS
PARAMETERS
0.87B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
256K tokens
CAPABILITIES
chat, coding, multilingual, vision
RELEASE DATE
2026-03-01
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.891.0 GB94%
Q5_K_S5.571.1 GB96%
Q5_K_M5.71.1 GB96%
Q6_K6.561.2 GB97%
Q8_08.51.4 GB100%
FP16162.2 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO29.7
MATH10.2
IFEval52.1
BBH16.3
MMMU49.0
GPQA6.7
MUSR4.6
BigCodeBench8.8
MMBench69.9
GPQA Diamond23.6
HLE4.9
AA Intelligence9.9
AA Coding1.0
aa_ifbench21.6
aa_tau265.2
aa_scicode2.9
aa_lcr6.7
§ 02RUN COMMAND

Run Qwen 3.5 0.8B locally with Ollama — needs 1.0 GB VRAM at Q4_K_M:

$ollama run qwen3.5:0.8b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M