Alibaba/Dense

AlibabaQwen3 14B

Qwen3 14B — dual-mode reasoning, very strong benchmarks for its size.

chatreasoningThinkingTool Use
14.8B
Parameters
32K
Context length
20
Benchmarks
10
Quantizations
600K
HF downloads
Architecture
Dense
Released
2025-04-28
Layers
40
KV Heads
8
Head Dim
128
Family
qwen

Quantization Options

QuantBitsVRAMQuality
Q3_K_M47.9 GBlow
Q3_K_L4.38.4 GBmoderate
IQ4_XS4.468.7 GBmoderate
Q4_K_S4.679.1 GBmoderate
Q4_K_M4.899.5 GBgood
Q5_K_S5.5710.8 GBgood
Q5_K_M5.711.0 GBgood
Q6_K6.5612.6 GBexcellent
Q8_08.516.2 GBlossless
FP161630.1 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 14B NOW

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

Community Ratings

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Benchmarks (20)

MATH91.0
HumanEval88.0
MATH-50087.1
IFEval82.0
BBH65.5
MMLU-PRO62.0
AIME58.0
AA Math58.0
GPQA52.0
GPQA Diamond47.0
BigCodeBench39.8
MUSR38.7
τ²-Bench32.2
LiveCodeBench28.0
SciCode26.5
IFBench23.9
AA Intelligence12.8
AA Coding12.4
Terminal-Bench5.3
HLE4.2

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3:14b-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 3080 10GB
10 GB VRAM • 760 GB/s
NVIDIA
$429
Intel Arc B570
10 GB VRAM • 456 GB/s
INTEL
$219
NVIDIA P102-101
10 GB VRAM • 320 GB/s
NVIDIA
NVIDIA CMP 170HX 10 GB
10 GB VRAM • 1560 GB/s
NVIDIA
NVIDIA CMP 50HX
10 GB VRAM • 560 GB/s
NVIDIA
NVIDIA CMP 90HX
10 GB VRAM • 760 GB/s
NVIDIA
NVIDIA RTX 2080 Ti
11 GB VRAM • 616 GB/s
NVIDIA
$350
NVIDIA GTX 1080 Ti
11 GB VRAM • 484 GB/s
NVIDIA
$200
NVIDIA RTX 5070
12 GB VRAM • 672 GB/s
NVIDIA
$549
NVIDIA RTX 4070 Ti
12 GB VRAM • 504 GB/s
NVIDIA
$799
NVIDIA RTX 4070 SUPER
12 GB VRAM • 504 GB/s
NVIDIA
$599
NVIDIA RTX 4070
12 GB VRAM • 504 GB/s
NVIDIA
$549
NVIDIA RTX 3080 Ti
12 GB VRAM • 912 GB/s
NVIDIA
$550
NVIDIA RTX 3080 12GB
12 GB VRAM • 912 GB/s
NVIDIA
$599
NVIDIA RTX 3060 12GB
12 GB VRAM • 360 GB/s
NVIDIA
$329
AMD RX 7700 XT
12 GB VRAM • 432 GB/s
AMD
$449
AMD RX 6700 XT
12 GB VRAM • 384 GB/s
AMD
$344
AMD RX 6750 XT
12 GB VRAM • 432 GB/s
AMD
$299
Intel Arc B580
12 GB VRAM • 456 GB/s
INTEL
$249
NVIDIA Tesla K40c
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40d
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40m
12 GB VRAM • 288 GB/s
NVIDIA

Find the best GPU for Qwen3 14B

Build Hardware for Qwen3 14B

Qwen3 14B — dual-mode reasoning, very strong benchmarks for its size.

▸ SPEC SHEET

Qwen3 14B14.8B Dense.

▸ SPECIFICATIONS
PARAMETERS
14.8B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
32K tokens
CAPABILITIES
chat, reasoning
RELEASE DATE
2025-04-28
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q3_K_M47.9 GB88%
Q3_K_L4.38.4 GB90%
IQ4_XS4.468.7 GB92%
Q4_K_S4.679.1 GB93%
Q4_K_M4.899.5 GB94%
Q5_K_S5.5710.8 GB96%
Q5_K_M5.711.0 GB96%
Q6_K6.5612.6 GB97%
Q8_08.516.2 GB100%
FP161630.1 GB100%
§ 01BENCHMARK SCORES
HumanEval88.0
MMLU-PRO62.0
MATH91.0
IFEval82.0
BBH65.5
GPQA52.0
MUSR38.7
BigCodeBench39.8
GPQA Diamond47.0
LiveCodeBench28.0
AIME58.0
MATH-50087.1
HLE4.2
AA Intelligence12.8
AA Coding12.4
AA Math58.0
aa_ifbench23.9
aa_terminal_bench5.3
aa_tau232.2
aa_scicode26.5
§ 02RUN COMMAND

Run Qwen3 14B locally with Ollama — needs 9.5 GB VRAM at Q4_K_M:

$ollama run qwen3:14b