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Qwen3-VL 8B Apple Silicon Benchmarks

The mid-size Qwen3-VL, 5.8 GB. Slower than E4B on both tests.

5.8 GBQwen3-VLmlx-community/Qwen3-VL-8B-Instruct-4bit

Who makes it

Qwen3-VL is Alibaba's vision-language line. GroundSlate runs the 4-bit MLX conversions from mlx-community.

Maker
Alibaba
Line
Qwen3-VL
Size
5.8 GB
Weights
mlx-community/Qwen3-VL-8B-Instruct-4bit

Against the other timed models

Still
5.89s
Prefill 2.02s
179 tok
46 tok/s decode
Video chunk
24.72s
737 tok

Still

seconds

Video chunk

seconds

On each chip

How long Qwen3-VL 8B takes on every chip, timed by the benchmark in the app on customers' own Macs. Every test on every chip

Describe a still

median time

The local vision model describes one frame, the way it does when you import footage.

  1. M632 GB
    7.10s

Still

Still prompt
You are a still-photo analyst. Look at this single photograph. Reply ONLY with compact JSON: {"desc":"a full paragraph of 4-6 factual sentences about this photograph. Cover, in this order: who is present and what they are doing; where this is and what kind of place it is; the notable objects and any text visible on signs, screens or clothing; the light, weather and time of day if they are apparent; and anything distinctive worth remembering about the moment. Write only what you can SEE. Do not speculate about feelings, relationships or backstory, do not pad with generic phrases, and never begin a sentence with 'The image shows' or 'This photo captures' — state the observation directly","subject":"<what the main subject is>","shot":"wide|medium|close|macro","setting":"indoor|outdoor|unknown","venue_type":"<what KIND of place this is: beach, restaurant, playground, living room, parking lot, forest trail, ... or unknown>","notable_objects":["<up to 5 salient objects>"]}. Answer "unknown" for setting or venue_type whenever you are unsure — abstaining costs you nothing. venue_type is a KIND of place, never a NAME: 'a beach', not 'Bondi Beach'. CRITICAL: never invent or guess the name of any person or business. Do NOT read a name off signage or clothing and attribute it to a person. Describe people generically — 'a woman', 'a child on a swing', 'a man in a red jacket'. Use a person's name ONLY if it is explicitly supplied to you in the context above; otherwise use no name at all. NAME OBJECTS SPECIFICALLY when you can actually identify them: 'a can of Coca-Cola' beats 'a drink', 'a yellow school bus' beats 'a vehicle'. Read product labels and use the brand when it is legible. But do NOT guess a specific dish, product, or place name if you are unsure — describe what you actually see (shape, colour, setting), not a confident wrong label. Trust any provided context over your assumptions.

Video chunk

Video chunk prompt
You are watching one continuous segment of a video (t=0.00s to 12.00s), given as 6 frames in chronological order:
Image 1 = t=0.00s
Image 2 = t=2.00s
Image 3 = t=4.00s
Image 4 = t=6.00s
Image 5 = t=8.00s
Image 6 = t=10.00s
No people have been identified in this segment.
Describe this video segment. Reply ONLY with JSON:
{"shots":[{"start":<sec>,"end":<sec>,"desc":"<=20 words","subject":"<s>","shot":"wide|medium|close|macro","motion":"static|slow|fast","center_x":<0..1>,"setting":"indoor|outdoor|unknown","venue_type":"<kind>","notable_objects":["<up to 5>"]}],"best_segment":{"start":<sec>,"end":<sec>,"reason":"<=15 words"},"track":[{"t":<sec>,"x":<0..1>} x6],"summary":"2-4 factual sentences. Each subject AT MOST ONCE.","visual_kind":"footage"}

MacBook Pro, Apple M3 Max, 64 GB unified memory. mlx-vlm 0.6.10. 2026-08-27.