FitMyLLM
Alibaba/Dense

AlibabaQwen3 32B

Qwen3 32B — flagship dense model with thinking mode. Near-frontier quality.

chatreasoningThinkingTool Use
32.8B
Parameters
32K
Context length
20
Benchmarks
14
Quantizations
700K
HF downloads
Architecture
Dense
Released
2025-04-28
Layers
64
KV Heads
8
Head Dim
128
Family
qwen

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
IQ3_XXS3.25
16.8 GB
13.8 + 3.0 KV
low
IQ3_XS3.5
17.8 GB
14.8 + 3.0 KV
low
Q3_K_S3.64
18.4 GB
15.4 + 3.0 KV
low
IQ3_M3.76
18.9 GB
15.9 + 3.0 KV
low
Q3_K_M4
19.9 GB
16.9 + 3.0 KV
low
Q3_K_L4.3
21.1 GB
18.1 + 3.0 KV
moderate
IQ4_XS4.46
21.8 GB
18.8 + 3.0 KV
moderate
Q4_K_S4.67
22.6 GB
19.6 + 3.0 KV
moderate
Q4_K_M4.89
23.5 GB
20.5 + 3.0 KV
good
Q5_K_S5.57
26.3 GB
23.3 + 3.0 KV
good
Q5_K_M5.7
26.9 GB
23.9 + 3.0 KV
good
Q6_K6.56
30.4 GB
27.4 + 3.0 KV
excellent
Q8_08.5
38.3 GB
35.3 + 3.0 KV
lossless
FP1616
69.1 GB
66.1 + 3.0 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 →
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Community Ratings

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Benchmarks (20)

Arena Elo1478
MATH-50096.1
MATH93.0
HumanEval92.0
IFEval85.0
MBPP77.0
AIME73.0
AA Math73.0
GPQA Diamond66.8
MMLU-PRO66.0
BBH58.3
GPQA58.0
LiveCodeBench54.6
BigCodeBench32.3
IFBench31.5
SciCode28.0
MUSR19.1
AA Intelligence16.5
AA Coding13.8
HLE8.3

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3:32b-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.

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
NVIDIA L40G
24 GB VRAM • 864 GB/s
NVIDIA
$5000

Find the best GPU for Qwen3 32B

Build Hardware for Qwen3 32B

Qwen3 32B — flagship dense model with thinking mode. Near-frontier quality.

▸ SPEC SHEET

Qwen3 32B32.8B Dense.

▸ SPECIFICATIONS
PARAMETERS
32.8B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
32K tokens
CAPABILITIES
chat, reasoning
RELEASE DATE
2025-04-28
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2513.8 GB82%
IQ3_XS3.514.8 GB84%
Q3_K_S3.6415.4 GB85%
IQ3_M3.7615.9 GB86%
Q3_K_M416.9 GB88%
Q3_K_L4.318.1 GB90%
IQ4_XS4.4618.8 GB92%
Q4_K_S4.6719.6 GB93%
Q4_K_M4.8920.5 GB94%
Q5_K_S5.5723.3 GB96%
Q5_K_M5.723.9 GB96%
Q6_K6.5627.4 GB97%
Q8_08.535.3 GB100%
FP161666.1 GB100%
§ 01BENCHMARK SCORES
HumanEval92.0
MMLU-PRO66.0
MATH93.0
IFEval85.0
BBH58.3
GPQA58.0
MUSR19.1
MBPP77.0
BigCodeBench32.3
Arena Elo1478.0
GPQA Diamond66.8
LiveCodeBench54.6
AIME73.0
MATH-50096.1
HLE8.3
AA Intelligence16.5
AA Coding13.8
AA Math73.0
aa_ifbench31.5
aa_scicode28.0
§ 02RUN COMMAND

Run Qwen3 32B locally with Ollama — needs 20.5 GB VRAM at Q4_K_M:

$ollama run qwen3:32b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M