tinyllama-1.1b-chat-v1.0

llama · 1.1B · Q3_K_M

LENOVO 20BUS00700 (Intel Core™ i5-5300U)

16 GB · Microsoft Windows 10 Professionnel 10.0.19045

Tested on March 5, 2026

Top 96% Compare

Global Score

33 /100

Not Rec.

Hardware Fit

76/100

Quality

14/100

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Hardware

Machine: LENOVO 20BUS00700
CPU: Intel Core™ i5-5300U
Cores: 4 total (4 perf)
Frequency: 2.3 GHz
RAM: 16 GB DDR3
GPU: Intel(R) HD Graphics 5500
OS: Microsoft Windows 10 Professionnel 10.0.19045
Arch: x64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 9.9
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.2
First chunk latency Delay before the first streamed chunk arrives from runtime: 3.2 s
Time to first token How fast the model starts responding — lower is better: 3.2 s
Load time Time to load the model into memory before inference: N/A
Memory usage RAM consumed during inference — percentage of total system memory: 0.5 GB (3%)
Total tokens Total number of tokens generated across all benchmark prompts: 1697

Score breakdown

Speed

32/50

Time to first token

16/20

Memory

28/30

Quality

Reasoning

2/20

Coding

1/20

Instruction following

6/20

Structured output

2/15

Math

1/15

Multilingual

2/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: LM Studio 0.4.6+1
Model format: GGUF
Hardware profile: ENTRY
Result hash: 38112cf03d8e7c354a3241157724b635cd4c39b093140a2788d54e6a57d57d9a

Interpretation

Hardware fit: 76/100. Overall suitability: NOT RECOMMENDED (Global 33/100). Category profile: Reasoning: Poor, Coding: Poor, Instruction Following: Weak, 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

Power: AC CPU load: avg 23% (peak 26%)

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