FitMyLLM
Alibaba/Mixture of Experts

AlibabaQwen 3.5 35B A3B

Qwen 3.5 35B A3B — MoE with multimodal, math, vision. Runs at 3B speed.

chatcodingreasoningmultilingualvisionmath
35B
Parameters (3B active)
256K
Context length
19
Benchmarks
14
Quantizations
2.5M
HF downloads
Architecture
MoE
Released
2026-02-01
Layers
40
KV Heads
2
Head Dim
256
Family
qwen

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
IQ3_XXS3.25
14.9 GB
14.7 + 0.2 KV
low
IQ3_XS3.5
16.0 GB
15.8 + 0.2 KV
low
Q3_K_S3.64
16.6 GB
16.4 + 0.2 KV
low
IQ3_M3.76
17.2 GB
16.9 + 0.2 KV
low
Q3_K_M4
18.2 GB
18.0 + 0.2 KV
low
Q3_K_L4.3
19.5 GB
19.3 + 0.2 KV
moderate
IQ4_XS4.46
20.2 GB
20.0 + 0.2 KV
moderate
Q4_K_S4.67
21.2 GB
20.9 + 0.2 KV
moderate
Q4_K_M4.89
22.1 GB
21.9 + 0.2 KV
good
Q5_K_S5.57
25.1 GB
24.9 + 0.2 KV
good
Q5_K_M5.7
25.7 GB
25.4 + 0.2 KV
good
Q6_K6.56
29.4 GB
29.2 + 0.2 KV
excellent
Q8_08.5
37.9 GB
37.7 + 0.2 KV
lossless
FP1616
70.7 GB
70.5 + 0.2 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 35B A3B NOW

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

Community Ratings

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Benchmarks (19)

Arena Elo1485
IFEval91.9
MMBench91.5
τ²-Bench89.2
MMLU-PRO85.3
GPQA Diamond81.9
MMMU75.1
IFBench72.5
AA Long Context62.7
MATH59.7
BBH58.3
SciCode37.7
BigCodeBench32.3
AA Intelligence30.7
Terminal-Bench26.5
MUSR19.1
AA Coding16.8
GPQA15.2
HLE12.8

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3.5:35b-a3b-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 Qwen 3.5 35B A3B

Build Hardware for Qwen 3.5 35B A3B

Qwen 3.5 35B A3B — MoE with multimodal, math, vision. Runs at 3B speed.

▸ SPEC SHEET

Qwen 3.5 35B A3B35B MoE.

▸ SPECIFICATIONS
PARAMETERS
35B (3B active)
ARCHITECTURE
Mixture of Experts
CONTEXT LENGTH
256K tokens
CAPABILITIES
chat, coding, reasoning, multilingual, vision, math
RELEASE DATE
2026-02-01
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2514.7 GB82%
IQ3_XS3.515.8 GB84%
Q3_K_S3.6416.4 GB85%
IQ3_M3.7616.9 GB86%
Q3_K_M418.0 GB88%
Q3_K_L4.319.3 GB90%
IQ4_XS4.4620.0 GB92%
Q4_K_S4.6720.9 GB93%
Q4_K_M4.8921.9 GB94%
Q5_K_S5.5724.9 GB96%
Q5_K_M5.725.4 GB96%
Q6_K6.5629.2 GB97%
Q8_08.537.7 GB100%
FP161670.5 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO85.3
MATH59.7
IFEval91.9
BBH58.3
MMMU75.1
GPQA15.2
MUSR19.1
BigCodeBench32.3
MMBench91.5
Arena Elo1485.0
GPQA Diamond81.9
HLE12.8
AA Intelligence30.7
AA Coding16.8
aa_ifbench72.5
aa_terminal_bench26.5
aa_tau289.2
aa_scicode37.7
aa_lcr62.7
§ 02RUN COMMAND

Run Qwen 3.5 35B A3B locally with Ollama — needs 21.9 GB VRAM at Q4_K_M:

$ollama run qwen3.5:35b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M