llama3.1:8b

llama · 8.0B · Q4_K_M

MacBook Pro (Apple M1 Pro)

16 GB · macOS 26.6.2

Tested on August 25, 2026 · Submitted by AAA

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Global Score

72 /100

Good

Hardware Fit

77/100

Quality

70/100

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Ollama

ollama pull llama3.1:8b

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ollama.com/library/llama3.1

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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: 21.5
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±1.3
First chunk latency Delay before the first streamed chunk arrives from runtime: 368 ms
Time to first token How fast the model starts responding — lower is better: 368 ms
Load time Time to load the model into memory before inference: 6.9 s
Memory usage RAM consumed during inference — percentage of total system memory: 12.9 GB (81%)
Total tokens Total number of tokens generated across all benchmark prompts: 1019

Score breakdown

Speed

47/50

Time to first token

20/20

Memory

10/30

Quality

Reasoning

12/20

Coding

18/20

Instruction following

13/20

Structured output

13/15

Math

4/15

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 0.32.15
Model format: GGUF
Hardware profile: ENTRY
Result hash: dfe5fab78668b6f5f3becef1f7d6fe7d6c48ddad2ee1053c84dfac2a387ba341

Interpretation

Hardware fit: 77/100. Overall suitability: GOOD (Global 72/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Adequate, Structured Output: Strong, Math: Weak, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 48% (peak 53%)

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