How do we deploy transformer models effectively?
Fine-tuning transformers is standard practice now.
How It Works:
Serve optimized transformer checkpoints via model servers (like Triton), apply distillation or quantization for production, and autoscale inference clusters.
Key Benefits:
- Enterprise-grade throughput
- Reduced memory footprint
- Consistent latency under load
Real-World Use Cases:
- Chat APIs with GPT-style models
- Document indexing with BERT embeddings
FAQs
What is model distillation?
How monitor GPU memory?
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