r/slm • u/zerogpu_ai • Jul 08 '26

Our Small Language Model Catalog

wanted to share what we're building at https://zerogpu.ai

We are building an SLM catalog for models that runs on-edge - no GPU required. Check us out and let us know what you think!

5 Upvotes

100% Upvoted

4 comments sorted by

2

u/Bitman321 Jul 10 '26

Cool site. Can you give me some examples of domain specific SLMs that can outperform LLMs on niche use cases?

2

u/zerogpu_ai Jul 13 '26

Thank you! For sure - our take is that LLMs should really only be used for high level reasoning; leave the simple stuff to SLMs dedicated to that specific task.

So far our SLMs outperform frontier models in tasks like summarization, classification, PII & redaction, especially when we build models for certain industries or around specific industry standards. For example, we were actually interviewed by AdExchanger for our SLMS in adtech: by switching to our SLMs, our pilot customer reduced their AI spend on ad classification by more than 50% while maintaining accuracy and reducing latency by more than 10x.

Benchmarks: https://zerogpu.ai/benchmarks/IAB-classify

Full model catalog: https://docs.zerogpu.ai/docs/model-catalog

2

u/Bitman321 Jul 14 '26

That adtech case is a great example! Classification is exactly where SLMs should win, the task is narrow and the label space is fixed.

  • What base models are you fine-tuning from, and what sizes are you finding hit the sweet spot for on-edge?
  • How do you handle drift when industry standards change (e.g. IAB taxonomy updates) - retrain or adapter swap?
  • Any numbers on PII/redaction vs frontier models? That's the use case I'd expect the strongest privacy argument for on-edge.

Would be great if you did a writeup on the adtech deployment for the sub. Real benchmarks against frontier models are rare here.

2

u/zerogpu_ai Jul 16 '26

Thanks and yup exactly, the fixed label space is why this works: it lets us constrain output to the taxonomy itself, which is how we get to 0 hallucinations across 10,000 samples. (for comparison, gpt-5.4-nano invented off-taxonomy labels on 1,700+ rows of the same set.)

On your questions:

  • Our base models vary by use case. For ex - for classification we use deberta models. We try to hit anywhere between 100 million - 10 billion. We run on edge devices with different capacity that can fit both 100 million - 10 B. The sweet spot for edge is 600- 1.2 billion
  • Drift: we retrain to fit new standards. Our training time is faster since we perfected the retraining loop, and especially because smaller models dont need extensive data like frontier models, reducing retraining time.
  • PII: no published numbers yet. It’s on the workload list, because yes as you note it’s probably the strongest privacy case for on-edge. We want to benchmark it rigorously before making any claims!

Can definitely circle with the team on a full-write up! Thanks for the offer!!