Alibaba/Dense

AlibabaQwen3.5-35B-A3B

chatThinkingTool Use
36B
Parameters
256K
Context length
16
Benchmarks
14
Quantizations
2.5M
HF downloads
Architecture
Dense
Released
2026-02-24
Layers
64
KV Heads
4
Head Dim
128
Family
qwen

Quantization Options

QuantBitsVRAMQuality
IQ3_XXS3.2515.1 GBlow
IQ3_XS3.516.2 GBlow
Q3_K_S3.6416.9 GBlow
IQ3_M3.7617.4 GBlow
Q3_K_M418.5 GBlow
Q3_K_L4.319.8 GBmoderate
IQ4_XS4.4620.6 GBmoderate
Q4_K_S4.6721.5 GBmoderate
Q4_K_M4.8922.5 GBgood
Q5_K_S5.5725.6 GBgood
Q5_K_M5.726.1 GBgood
Q6_K6.5630.0 GBexcellent
Q8_08.538.7 GBlossless
FP161672.5 GBlossless

Select your GPU above to see speed estimates and compatibility for each quantization.

READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN QWEN3.5-35B-A3B NOW

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

Community Ratings

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Benchmarks (16)

τ²-Bench89.2
GPQA Diamond84.5
IFEval78.9
IFBench72.5
AA Long Context62.7
MATH59.7
BBH58.3
MMLU-PRO52.9
SciCode37.7
AA Intelligence37.1
BigCodeBench32.3
AA Coding30.3
Terminal-Bench26.5
HLE19.7
MUSR19.1
GPQA15.2

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3:36.0b-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 RTX 4090
24 GB VRAM • 1008 GB/s
NVIDIA
$1599
NVIDIA RTX 3090 Ti
24 GB VRAM • 1008 GB/s
NVIDIA
$999
NVIDIA RTX 3090
24 GB VRAM • 936 GB/s
NVIDIA
$850
AMD RX 7900 XTX
24 GB VRAM • 960 GB/s
AMD
$999
Apple M4 Pro (24GB)
24 GB VRAM • 273 GB/s
APPLE
$1399
NVIDIA L4 24GB
24 GB VRAM • 300 GB/s
NVIDIA
$2500
NVIDIA A10 24GB
24 GB VRAM • 600 GB/s
NVIDIA
$3500
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 Quadro RTX 6000
24 GB VRAM • 672 GB/s
NVIDIA
$4000
NVIDIA GeForce RTX 3090
24 GB VRAM • 936 GB/s
NVIDIA
$1499
NVIDIA A10 PCIe
24 GB VRAM • 600 GB/s
NVIDIA
NVIDIA A10G
24 GB VRAM • 600 GB/s
NVIDIA
NVIDIA RTX A5000
24 GB VRAM • 768 GB/s
NVIDIA
$2500
NVIDIA GeForce RTX 4090
24 GB VRAM • 1010 GB/s
NVIDIA
$1599
NVIDIA L40 CNX
24 GB VRAM • 864 GB/s
NVIDIA
$5000
NVIDIA L40G
24 GB VRAM • 864 GB/s
NVIDIA
$5000
NVIDIA A30 PCIe
24 GB VRAM • 933 GB/s
NVIDIA
NVIDIA A30X
24 GB VRAM • 1220 GB/s
NVIDIA

Find the best GPU for Qwen3.5-35B-A3B

Build Hardware for Qwen3.5-35B-A3B

Read the full model card for detailed information about this model.

▸ SPEC SHEET

Qwen3.5-35B-A3B36B Dense.

▸ SPECIFICATIONS
PARAMETERS
36B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
256K tokens
CAPABILITIES
chat
RELEASE DATE
2026-02-24
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2515.1 GB82%
IQ3_XS3.516.2 GB84%
Q3_K_S3.6416.9 GB85%
IQ3_M3.7617.4 GB86%
Q3_K_M418.5 GB88%
Q3_K_L4.319.8 GB90%
IQ4_XS4.4620.6 GB92%
Q4_K_S4.6721.5 GB93%
Q4_K_M4.8922.5 GB94%
Q5_K_S5.5725.6 GB96%
Q5_K_M5.726.1 GB96%
Q6_K6.5630.0 GB97%
Q8_08.538.7 GB100%
FP161672.5 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO52.9
MATH59.7
IFEval78.9
BBH58.3
GPQA15.2
MUSR19.1
BigCodeBench32.3
aa_ifbench72.5
aa_terminal_bench26.5
aa_tau289.2
aa_scicode37.7
aa_lcr62.7
GPQA Diamond84.5
HLE19.7
AA Intelligence37.1
AA Coding30.3
§ 02RUN COMMAND

Run Qwen3.5-35B-A3B locally with Ollama — needs 22.5 GB VRAM at Q4_K_M:

$ollama run qwen3:36b