nvidia-nemotron-3-nano-4b

nemotron_h · 4B · Q4_K_M

Gigabyte Technology Co., Ltd. H310M S2 2.0 (Intel Core™ i3-9100F)

16 GB · Майкрософт Windows 10 Pro 10.0.19045

Tested on October 24, 2026

Top 30% Compare

Global Score

83 /100

Excellent

Hardware Fit

100/100

Quality

75/100

Get this model

Get it in LM Studio

Search and download models directly from the app

🤗

Find on HuggingFace

MLX versions & conversions

Hardware

Machine: Gigabyte Technology Co., Ltd. H310M S2 2.0
CPU: Intel Core™ i3-9100F
Cores: 4 total (4 perf)
Frequency: 3.6 GHz
RAM: 16 GB DDR4
GPU: Virtual Desktop Monitor, NVIDIA GeForce GTX 1650
OS: Майкрософт Windows 10 Pro 10.0.19045
Arch: x64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 28.9
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±6.8
First chunk latency Delay before the first streamed chunk arrives from runtime: 22 ms
Time to first token How fast the model starts responding — lower is better: 580 ms
Load time Time to load the model into memory before inference: N/A
Memory usage RAM consumed during inference — percentage of total system memory: 3.8 GB (24%)
Total tokens Total number of tokens generated across all benchmark prompts: 1085

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

15/20

Coding

12/20

Instruction following

16/20

Structured output

14/15

Math

8/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: LM Studio 0.4.11+1
Model format: GGUF
Hardware profile: ENTRY
Result hash: 967a54123fef32b699ecd0593ba2cde3fd2be8802b8acee94fba16d0fde3615b

Interpretation

Hardware fit: 100/100. Overall suitability: EXCELLENT (Global 83/100). Category profile: Reasoning: Adequate, Coding: Adequate, Instruction Following: Strong, Structured Output: Strong, Math: Adequate, Multilingual: Strong.

Warnings

Model memory footprint is estimated via LM Studio CLI rather than measured from a fresh load.

Bench Environment

CPU load: avg 47% (peak 63%)

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