qwen3-vl-4b-instruct

qwen3vl · 4B · Q4_K_M

Mac Studio (Apple M2 Max)

96 GB · macOS 26.1

Tested on April 20, 2026 · Submitted by Frank

Top 8% Compare

Global Score

87 /100

Excellent

Hardware Fit

100/100

Quality

81/100

Get this model

Get it in LM Studio

Search and download models directly from the app

🤗

Find on HuggingFace

MLX versions & conversions

Hardware

Machine: Mac Studio
CPU: Apple M2 Max
Cores: 12 total (8 perf + 4 eff)
Frequency: 2.4 GHz
RAM: 96 GB LPDDR5
GPU: Apple M2 Max
OS: macOS 26.1
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 71.6
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±1.0
First chunk latency Delay before the first streamed chunk arrives from runtime: 5 ms
Time to first token How fast the model starts responding — lower is better: 157 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: 20.5 GB (21%)
Total tokens Total number of tokens generated across all benchmark prompts: 1091

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

15/20

Coding

17/20

Instruction following

17/20

Structured output

15/15

Math

8/15

Multilingual

9/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: LM Studio 0.4.10+1
Model format: GGUF
Hardware profile: HIGH-END
Result hash: 0d9224a994d56c0b5182c5846de6f7266456da691a15965c2f1fbaa08226c4d1

Interpretation

Hardware fit: 100/100. Overall suitability: EXCELLENT (Global 87/100). Category profile: Reasoning: Strong, Coding: Strong, 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

Power: AC CPU load: avg 23% (peak 28%)

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