hf.co/LiquidAI/LFM2.5-2.6B-GGUF:F16

lfm2 · 2.7B · unknown

MacBook Pro (Apple M1 Pro)

16 GB · macOS 26.6.2

Tested on August 26, 2026 · Submitted by AAA

Top 78% Compare

Global Score

61 /100

Good

Hardware Fit

97/100

Quality

46/100

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Ollama

ollama pull hf.co/LiquidAI/LFM2.5-2.6B-GGUF:F16

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ollama.com/library/hf.co/LiquidAI/LFM2.5-2.6B-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: 31.3
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: 131 ms
Time to first token How fast the model starts responding — lower is better: 131 ms
Load time Time to load the model into memory before inference: 4.2 s
Memory usage RAM consumed during inference — percentage of total system memory: 6.6 GB (41%)
Total tokens Total number of tokens generated across all benchmark prompts: 1428

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

27/30

Quality

Reasoning

14/20

Coding

13/20

Instruction following

3/20

Structured output

0/15

Math

14/15

Multilingual

2/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.32.15
Model format: GGUF
Hardware profile: ENTRY
Result hash: 76a60b7bcfbe747c2b9e47511f465a930429deb606c7352be379ad1d6fe6e065

Interpretation

Hardware fit: 97/100. Overall suitability: GOOD (Global 61/100). Category profile: Reasoning: Adequate, Coding: Adequate, Instruction Following: Poor, Structured Output: Poor, Math: Strong, Multilingual: Poor.

Bench Environment

Power: AC CPU load: avg 33% (peak 38%)

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