FitMyLLM
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

Context length:
QuantBitsVRAM @ 16KQuality
Q3_K_M4
9.8 GB
7.9 + 1.9 KV
low
Q3_K_L4.3
10.3 GB
8.4 + 1.9 KV
moderate
IQ4_XS4.46
10.6 GB
8.7 + 1.9 KV
moderate
Q4_K_S4.67
11.0 GB
9.1 + 1.9 KV
moderate
Q4_K_M4.89
11.4 GB
9.5 + 1.9 KV
good
Q5_K_S5.57
12.7 GB
10.8 + 1.9 KV
good
Q5_K_M5.7
12.9 GB
11.0 + 1.9 KV
good
Q6_K6.56
14.5 GB
12.6 + 1.9 KV
excellent
Q8_08.5
18.1 GB
16.2 + 1.9 KV
lossless
FP1616
32.0 GB
30.1 + 1.9 KV
lossless

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

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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 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 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
§ 03COMPATIBLE GPUs
30 @ Q4_K_M