FitMyLLM
Alibaba/Dense

AlibabaQwen 3.6 27B

> This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

chatcodingreasoningmultilingualvisionmath
27B
Parameters
256K
Context length
16
Benchmarks
10
Quantizations
0
Architecture
Dense
Released
2026-04-22
Layers
64
KV Heads
4
Head Dim
256
Family
qwen

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q3_K_M4
14.7 GB
14.0 + 0.8 KV
low
Q3_K_L4.3
15.8 GB
15.0 + 0.8 KV
moderate
IQ4_XS4.46
16.3 GB
15.5 + 0.8 KV
moderate
Q4_K_S4.67
17.0 GB
16.3 + 0.8 KV
moderate
Q4_K_M4.89
17.7 GB
17.0 + 0.8 KV
good
Q5_K_S5.57
20.0 GB
19.3 + 0.8 KV
good
Q5_K_M5.7
20.5 GB
19.7 + 0.8 KV
good
Q6_K6.56
23.4 GB
22.6 + 0.8 KV
excellent
Q8_08.5
29.9 GB
29.2 + 0.8 KV
lossless
FP1616
55.2 GB
54.5 + 0.8 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 3.6 27B NOW

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

Community Ratings

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Benchmarks (16)

τ²-Bench93.6
GPQA Diamond82.9
MATH55.4
AA Long Context55.0
IFBench45.7
MMLU-PRO40.3
BBH38.5
SciCode37.3
AA Intelligence37.1
IFEval34.2
BigCodeBench32.3
AA Coding26.6
MUSR25.2
Terminal-Bench21.2
GPQA18.1
HLE13.6

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3.6:27b-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 M3 Pro (18GB)
18 GB VRAM • 150 GB/s
APPLE
$1599
NVIDIA RTX A4500
20 GB VRAM • 640 GB/s
NVIDIA
$2000
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

Find the best GPU for Qwen 3.6 27B

Build Hardware for Qwen 3.6 27B
▸ SPEC SHEET

Qwen 3.6 27B27B Dense.

▸ SPECIFICATIONS
PARAMETERS
27B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
256K tokens
CAPABILITIES
chat, coding, reasoning, multilingual, vision, math
RELEASE DATE
2026-04-22
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q3_K_M414.0 GB88%
Q3_K_L4.315.0 GB90%
IQ4_XS4.4615.5 GB92%
Q4_K_S4.6716.3 GB93%
Q4_K_M4.8917.0 GB94%
Q5_K_S5.5719.3 GB96%
Q5_K_M5.719.7 GB96%
Q6_K6.5622.6 GB97%
Q8_08.529.2 GB100%
FP161654.5 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO40.3
MATH55.4
IFEval34.2
BBH38.5
GPQA18.1
MUSR25.2
BigCodeBench32.3
GPQA Diamond82.9
HLE13.6
AA Intelligence37.1
AA Coding26.6
aa_ifbench45.7
aa_terminal_bench21.2
aa_tau293.6
aa_scicode37.3
aa_lcr55.0
§ 02RUN COMMAND

Run Qwen 3.6 27B locally with Ollama — needs 17.0 GB VRAM at Q4_K_M:

$ollama run qwen3.6:27b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M