glm4:9b

chatglm · 9.4B · Q4_0

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 6, 2026

Top 29% Compare

Global Score

76 /100

Good

Hardware Fit

88/100

Quality

71/100

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Ollama

ollama pull glm4:9b

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ollama.com/library/glm4

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GGUF versions & quantizations

Hardware

Machine: MacBook Air
CPU: Apple M4
Cores: 10 total (4 perf + 6 eff)
Frequency: 2.4 GHz
RAM: 32 GB LPDDR5
GPU: Apple M4
OS: macOS 26.3
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 19.0
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.1
First chunk latency Delay before the first streamed chunk arrives from runtime: 290 ms
Time to first token How fast the model starts responding — lower is better: 290 ms
Load time Time to load the model into memory before inference: 0.8 s
Memory usage RAM consumed during inference — percentage of total system memory: 8.1 GB (25%)
Total tokens Total number of tokens generated across all benchmark prompts: 1045

Score breakdown

Speed

38/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

14/20

Coding

15/20

Instruction following

14/20

Structured output

13/15

Math

5/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.17.6
Model format: GGUF
Hardware profile: BALANCED
Result hash: 65f7a96f804f2b68239f0dc1e587f58a79055a2efa2acdde30b6018e6772e83f

Interpretation

Hardware fit: 88/100. Overall suitability: GOOD (Global 76/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Adequate, Structured Output: Strong, Math: Weak, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 5% (peak 11%)

Run yours now

$ npm install -g metrillm@latest

$ metrillm

Requires Node 20+ and Ollama or LM Studio running

Or run without installing: npx metrillm@latest