starcoder2:3b

starcoder2 · 3B · Q4_0

NVIDIA NVIDIA Jetson Orin Nano Engineering Reference Developer Kit Super (Cortex-A78AE)

7 GB · Ubuntu 22.04.5 LTS

Tested on June 26, 2026

Top 93% Compare

Global Score

35 /100

Not Rec.

Hardware Fit

100/100

Quality

7/100

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ollama pull starcoder2:3b

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ollama.com/library/starcoder2

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GGUF versions & quantizations

Hardware

Machine: NVIDIA NVIDIA Jetson Orin Nano Engineering Reference Developer Kit Super
CPU: Cortex-A78AE
Cores: 6 total (6 perf)
Frequency: 1.04 GHz
RAM: 7 GB
GPU: Integrated / Unknown
OS: Ubuntu 22.04.5 LTS
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 31.5
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: 392 ms
Time to first token How fast the model starts responding — lower is better: 392 ms
Load time Time to load the model into memory before inference: 4.8 s
Memory usage RAM consumed during inference — percentage of total system memory: 1.8 GB (24%)
Total tokens Total number of tokens generated across all benchmark prompts: 1090

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

0/20

Coding

3/20

Instruction following

1/20

Structured output

0/15

Math

1/15

Multilingual

2/10

Category levels

Reasoning: Poor Coding: Poor Instruction Following: Poor Structured Output: Poor Math: Poor Multilingual: Poor

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.30.10
Model format: GGUF
Hardware profile: ENTRY
Result hash: 1b2eefecd4aa9376073310d11076fc0937954ade7080a2a81a1c297172314589

Interpretation

Hardware fit: 100/100. Overall suitability: NOT RECOMMENDED (Global 35/100). Category profile: Reasoning: Poor, Coding: Poor, Instruction Following: Poor, Structured Output: Poor, Math: Poor, Multilingual: Poor. Warning: model produced very low accuracy on quality tasks — results may be unusable despite good hardware performance.

Warnings

Model produced very low accuracy on quality tasks — results may be unusable despite good hardware performance.

Bench Environment

Thermal: nominal CPU load: avg 4% (peak 11%)

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$ npm install -g metrillm@latest

$ metrillm

Requires Node 20+ and Ollama or LM Studio running

Or run without installing: npx metrillm@latest