yi-coder:9b

llama · 8.8B · Q4_0

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 6, 2026

Top 45% Compare

Global Score

70 /100

Good

Hardware Fit

89/100

Quality

62/100

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Ollama

ollama pull yi-coder:9b

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ollama.com/library/yi-coder

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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: 19.4
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: 287 ms
Time to first token How fast the model starts responding — lower is better: 287 ms
Load time Time to load the model into memory before inference: 0.7 s
Memory usage RAM consumed during inference — percentage of total system memory: 9.6 GB (30%)
Total tokens Total number of tokens generated across all benchmark prompts: 1112

Score breakdown

Speed

39/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

12/20

Coding

16/20

Instruction following

13/20

Structured output

7/15

Math

7/15

Multilingual

7/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.17.6
Model format: GGUF
Hardware profile: BALANCED
Result hash: 8e16ac7864a3a3a50f0e091c49ff325c9c3a05836cc1e2684b3d2d7b8fd82478

Interpretation

Hardware fit: 89/100. Overall suitability: GOOD (Global 70/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Adequate, Structured Output: Weak, Math: Weak, Multilingual: Adequate.

Bench Environment

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

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