Every "free vision AI" recommendation conflates three very different things: running a model on your own GPU (Ollama), calling someone else's free API (Hugging Face Inference), and borrowing a free GPU notebook (Colab). Same model, three delivery mechanisms, wildly different failure modes.
So I ran the same 50-image benchmark through all three: describe a screenshot of a UI bug, extract the error text. Model: Llama-3.2-11B-Vision where available, Qwen2-VL-7B as the Ollama local pick. Here is what actually happened.
The setup
-
Ollama:
qwen2-vl:7bon my RTX 3060 12GB, fully offline - Hugging Face Inference API: free tier, serverless endpoint
- Colab free: T4 GPU, same Qwen2-VL weights loaded manually
Prompt identical across all three. 50 screenshots from my issue tracker.
Results
| Ollama (local) | HF Inference (free) | Colab (free T4) | |
|---|---|---|---|
| Wall time, 50 images | 14 min | 47 min | 31 min |
| Per-image latency | ~17s | ~56s (avg) | ~37s |
| Runs killed mid-benchmark | 0 | 2 (rate limit) | 1 (GPU timeout) |
| Error-text extraction accuracy | 82% | 80% | 82% |
| Cost | $0 (+ electricity) | $0 (until it stops working) | $0 (until it disconnects) |
| Cold start | 0s (model stays loaded) | 20–60s | ~4 min (runtime spin-up) |
The accuracy is basically a wash — same model class, same prompts. The entire difference is reliability.
What broke where
HF free Inference is great until your 31st request in an hour returns 429 and your batch script dies. Retry logic fixes it, but then your "47 minutes" becomes 90. It's a demo tier wearing a production costume.
Colab's free T4 is the opposite problem: no rate limits, real GPU, but the runtime disconnects if you look away too long, and my second run got a "you have used too much GPU recently" cooldown. It also can't stay loaded — every session starts with a 4-minute model load.
Ollama just… ran. Fourteen minutes, zero drama, worked on a train with no wifi. The 3060 isn't fast, but a predictable 17s/image beats an unpredictable anything.
My actual recommendation
- Prototyping, 10 images, once: HF free API. Don't even install anything.
- A batch you need done today: Ollama, if you have 8GB+ VRAM.
- Colab free: only if your laptop is a Chromebook. Treat it as borrowed hardware, because it is.
The framing "which free vision model" is the wrong question. The model is the same everywhere. The question is which failure mode you can tolerate: rate limits, disconnects, or buying a used GPU.
I prototype local-model scripts with an offline AI coding assistant — MonkeyCode: https://ly.cyberserval.tech/iIETXiF
Which free tier has burned you the worst — the rate limits, the disconnects, or the cold starts?
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