LOADING DATASHEET
LOADING DATASHEET
by Alibaba
RANKED #63 OF 316 ASSISTANTS · OVERALL #86 OF 493 RANKED · VIBE SCORE 6.2 · 687 VOICES ·
COPY BADGE pastes a README snippet with the rank SVG.
Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced tex
17 mentions
18 mentions
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Open weights. You can use a hosted service, or download it and run it yourself, free.
Open weights: download Qwen3 VL 32B Instruct once and it's yours. No subscription, no rate limits, works offline.
32B parameters, a Q4_K_M GGUF file is about 20 GB
NVIDIA GeForce RTX 3060 12GB (with 32 GB system RAM offloadi
12 GB VRAM + 32 GB RAM
~$220 USEDQ4_K_M, ~6 tok/s, 8k context (partial CPU offload)
NVIDIA GeForce RTX 3090 24GB
24 GB VRAM
~$700 USEDQ4_K_M or AWQ 4-bit, ~26 tok/s, 16k context (full GPU offload)
Apple Mac Studio M2 Ultra (128 GB Unified Memory)
128 GB UNIFIED MEMORY
~$3500 REFURBISHEDFP16 or Q8_0, ~32 tok/s, 128k context
The three cards above are researched picks. Search the machine you actually have — we only say yes if it should reply at a usable speed, not just load the weights and crawl.
HARDWARE PICKS & PRICES ARE RESEARCHED FROM THE LIVE WEB AND REFRESHED AUTOMATICALLY. TREAT THEM AS BALLPARK, NOT GOSPEL.
Everyday jobs on each of those rigs: how long Qwen3 VL 32B Instruct takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMNVIDIA GeForce RTX 3060 12GB (with 32 GB system RAM offloadi | SWEET SPOTNVIDIA GeForce RTX 3090 24GB | FULL POWERApple Mac Studio M2 Ultra (128 GB Unified Memory) |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~83 S★★★★★ | ~19 S★★★★★ | ~16 S★★★★★ |
| Summarize a documenta long report boiled down to the points that matter | ~4.2 MIN★★★★★ | ~58 S★★★★★ | ~47 S★★★★★ |
| Build a websitea small landing page, markup and styles together | ~22 MIN★★★★★ | ~5.1 MIN★★★★★ | ~4.2 MIN★★★★★ |
| Build a backendan API with routes, storage and tests | ~69 MIN★★★★★ | ~16 MIN★★★★★ | ~13 MIN★★★★★ |
| Build a gamea playable browser game in one file | ~42 MIN★★★★★ | ~9.6 MIN★★★★★ | ~7.8 MIN★★★★★ |
STARS ARE THE COMMUNITY SCORE FOR THAT KIND OF WORK, DOCKED FOR HOW SQUASHED THE WEIGHTS GET AT EACH TIER. TIMES ARE COMPUTED FROM RESEARCHED SPEEDS. BALLPARK, NOT GOSPEL.
People are mostly frustrated with Qwen3 VL 32B Instruct right now. Biggest gripe: working when you need it.
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Checked picks first: tags say why you would switch, stars say how fully each one stands in for Qwen3 VL 32B Instruct.
Krea 2 Identity Edit LoRA
Krea 2 Turbo — Native ComfyUI Workflow + FP8 Weights (12GB, Drag & Drop)
Ollama Models Ranked by VRAM Requirements
As promised Krea 2 Turbo + "Raw" Quantized in FP8, MXFP8, NVFP4, INT8 and Convrot INT8!
I released two Krea 2 functional LoRAs: identity reference and positional outpainting (weights + Diffusers pipelines)
Krea 2 Turbo does the Ghibli art style quite well out of the box
Repost because not everyone can read blurry photos or long threads: MiniMax H3 is confirmed to be 33.1B parameters, but testers found out it's effectively 20.1B due to a 2688-dimensional oversight (and that's a VERY good thing for us!)
How I built an AI news agency that runs itself - over 1B tokens processed locally
microsoft/AesCode 8B and 32B
I ran Gemma 4 26B vs Qwen 3.5 27B across 18 real local business tests on my RTX 4090. Gemma won 13 to 5.
Stop using T2V & Best Practices IMO (LTX Video / ComfyUI Guide)
Show HN: Off Grid – Run AI text, image gen, vision offline on your phone