Classification is a bread-and-butter ML workload – intent detection, sentiment, topic tagging, spam, routing. On the RTX 5060 Ti 16GB at our hosting, you can classify at scale.
Contents
Approaches
- Fine-tuned classifier: DeBERTa-v3 or RoBERTa head on your labels. Fast, cheap, high accuracy if you have labelled data.
- Zero-shot via LLM: Phi-3 mini or Llama 3 8B with prompt-as-instruction. No training data, flexible.
- Embedding + kNN: BGE-base embedding then nearest-label lookup. Great for fuzzy matches and for when labels change often.
Throughput
| Method | Items/sec | Daily capacity |
|---|---|---|
| DeBERTa-v3-large batch 32 | 800 | ~69M/day |
| DeBERTa-v3-base batch 64 | 2,400 | ~207M/day |
| Phi-3 mini FP8 (structured prompt) | 220 | ~19M/day |
| Llama 3 8B FP8 | 85 | ~7.3M/day |
| BGE-base embed + kNN | 10,000 | ~864M/day |
Which Approach to Pick
- Have > 5k labelled samples: fine-tune DeBERTa. Fast and accurate.
- Labels change weekly: embedding + kNN. No retrain needed when labels change.
- Zero labelled data: Phi-3 zero-shot. Fine for prototyping and small scale.
- Complex labels needing reasoning: Llama 3 8B prompt. Slower but handles nuance.
- Hybrid: DeBERTa for 95% confident cases, LLM for the uncertain 5%.
Recommendation: start with DeBERTa fine-tuning. It’s boring, fast, and correct for most classification problems.
Classification at Scale on Blackwell 16GB
200M items/day on DeBERTa-base. UK dedicated hosting.
Order the RTX 5060 Ti 16GBSee also: content moderation, embedding throughput, Phi-3 benchmark, Phi-3 guide.
