text-embedding-nomic-embed-text-v1.5

nomic-bert · Q4_K_M

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 3, 2026 · Submitted by Topaz750

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Global Score

59 /100

Marginal

Hardware Fit

94/100

Quality

44/100

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ollama pull text-embedding-nomic-embed-text-v1.5

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ollama.com/library/text-embedding-nomic-embed-text-v1.5

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GGUF versions & quantizations

Hardware

Machine: MacBook Air
CPU: Apple M4
Cores: 10 total (4 perf + 6 eff)
Frequency: 2.4 GHz
RAM: 32 GB LPDDR5
GPU: Apple M4
OS: macOS 26.3
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 45.3
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±9.9
First chunk latency Delay before the first streamed chunk arrives from runtime: 2.4 s
Time to first token How fast the model starts responding — lower is better: 2.4 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: 0.0 GB (0%)
Total tokens Total number of tokens generated across all benchmark prompts: 1087

Score breakdown

Speed

40/50

Time to first token

25/20

Memory

29/30

Quality

Reasoning

10/20

Coding

3/20

Instruction following

11/20

Structured output

12/15

Math

3/15

Multilingual

5/10

Category levels

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

Metadata

Spec version: 0.2.0
Runtime: LM Studio
Model format: GGUF
Hardware profile: BALANCED
Result hash: 9b982801de1e4849b885160bc08eb29b7c7477287602664030acf1ac87c4a15e

Interpretation

Hardware fit: 94/100. Overall suitability: MARGINAL (Global 59/100). Category profile: Reasoning: Weak, Coding: Poor, Instruction Following: Adequate, Structured Output: Strong, Math: Poor, Multilingual: Adequate.

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