hf.co/empero-ai/Qwen3.8-2B-Distill-GGUF:BF16

qwen35 · 1.94B · BF16

MacBook Pro (Apple M1 Pro)

16 GB · macOS 26.6.2

Tested on August 25, 2026 · Submitted by AAA

Top 51% Compare

Global Score

76 /100

Good

Hardware Fit

100/100

Quality

66/100

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Ollama

ollama pull hf.co/empero-ai/Qwen3.8-2B-Distill-GGUF:BF16

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ollama.com/library/hf.co/empero-ai/Qwen3.8-2B-Distill-GGUF

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GGUF versions & quantizations

Hardware

Machine: MacBook Pro
CPU: Apple M1 Pro
Cores: 10 total (8 perf + 2 eff)
Frequency: 2.4 GHz
RAM: 16 GB LPDDR5
GPU: Apple M1 Pro
OS: macOS 26.6.2
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 40.3
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: 101 ms
Time to first token How fast the model starts responding — lower is better: 101 ms
Load time Time to load the model into memory before inference: 2.7 s
Memory usage RAM consumed during inference — percentage of total system memory: 4.5 GB (28%)
Total tokens Total number of tokens generated across all benchmark prompts: 1112

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

11/20

Coding

10/20

Instruction following

12/20

Structured output

15/15

Math

8/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.32.15
Model format: GGUF
Hardware profile: ENTRY
Result hash: d5e35bba5c270b2d6b01c6fc1df89db332164064f906e264a2b60f4a9d3a2f54

Interpretation

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

Bench Environment

Power: AC CPU load: avg 37% (peak 58%)

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