FitMyLLM
Alibaba/Dense

AlibabaQwen 1.5 1.8B

Qwen 1.5 1.8B — compact model for basic Chinese-English tasks.

chat
1.8B
Parameters
32K
Context length
7
Benchmarks
6
Quantizations
0
Architecture
Dense
Released
2024-02-04
Layers
24
KV Heads
16
Head Dim
128
Family
qwen

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q4_K_M4.89
3.8 GB
1.6 + 2.3 KV
good
Q5_K_S5.57
4.0 GB
1.7 + 2.3 KV
good
Q5_K_M5.7
4.0 GB
1.8 + 2.3 KV
good
Q6_K6.56
4.2 GB
2.0 + 2.3 KV
excellent
Q8_08.5
4.7 GB
2.4 + 2.3 KV
lossless
FP1616
6.3 GB
4.1 + 2.3 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 1.5 1.8B NOW

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

Community Ratings

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Benchmarks (7)

IFEval44.8
BigCodeBench27.0
MATH22.1
MMLU-PRO20.0
BBH19.8
MUSR3.2
GPQA0.0

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen:1.8b-chat-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.

Find the best GPU for Qwen 1.5 1.8B

Build Hardware for Qwen 1.5 1.8B

Qwen 1.5 1.8B — compact model for basic Chinese-English tasks.

▸ SPEC SHEET

Qwen 1.5 1.8B1.8B Dense.

▸ SPECIFICATIONS
PARAMETERS
1.8B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
32K tokens
CAPABILITIES
chat
RELEASE DATE
2024-02-04
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.891.6 GB94%
Q5_K_S5.571.7 GB96%
Q5_K_M5.71.8 GB96%
Q6_K6.562.0 GB97%
Q8_08.52.4 GB100%
FP16164.1 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO20.0
MATH22.1
IFEval44.8
BBH19.8
MUSR3.2
BigCodeBench27.0
GPQA0.0
§ 02RUN COMMAND

Run Qwen 1.5 1.8B locally with Ollama — needs 1.6 GB VRAM at Q4_K_M:

$ollama run qwen:1.8b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M