qwen3-coder:30b

qwen3moe · 30.5B · Q4_K_M

MacBook Pro (Apple M4 Max)

64 GB · macOS 26.5.1

Tested on June 19, 2026

Top 8% Compare

Global Score

89 /100

Excellent

Hardware Fit

96/100

Quality

86/100

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Ollama

ollama pull qwen3-coder:30b

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ollama.com/library/qwen3-coder

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GGUF versions & quantizations

Hardware

Machine: MacBook Pro
CPU: Apple M4 Max
Cores: 16 total (12 perf + 4 eff)
Frequency: 2.4 GHz
RAM: 64 GB LPDDR5
GPU: Apple M4 Max
OS: macOS 26.5.1
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 89.9
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.7
First chunk latency Delay before the first streamed chunk arrives from runtime: 188 ms
Time to first token How fast the model starts responding — lower is better: 188 ms
Load time Time to load the model into memory before inference: 4.0 s
Memory usage RAM consumed during inference — percentage of total system memory: 30.2 GB (47%)
Total tokens Total number of tokens generated across all benchmark prompts: 1006

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

26/30

Quality

Reasoning

14/20

Coding

19/20

Instruction following

17/20

Structured output

15/15

Math

11/15

Multilingual

10/10

Category levels

Reasoning: Adequate Coding: Strong Instruction Following: Strong Structured Output: Strong Math: Adequate Multilingual: Strong

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.30.8
Model format: GGUF
Hardware profile: HIGH-END
Result hash: 41d8971dc83ee72a542489eac6ca1d041c69b2e31b1a05dcb73caee54b53a058

Interpretation

Hardware fit: 96/100. Overall suitability: EXCELLENT (Global 89/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Strong, Structured Output: Strong, Math: Adequate, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 10% (peak 13%)

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Or run without installing: npx metrillm@latest