1. The Small Language Model (SLM) Advantage

For highly repetitive enterprise tasks—such as document extraction, classification, and structured API translation—massive 70B+ parameter models introduce unnecessary latency, memory footprint, and compute costs.

Targeted 8B parameter models fine-tuned on domain-specific datasets deliver higher accuracy for structured JSON extraction at a fraction of the inference latency.

2. LoRA Fine-Tuning Workflow

Using Low-Rank Adaptation (LoRA), we train rank-8 or rank-16 parameter matrices on specialized input-output schema pairs, freezing base model weights while embedding domain precision.

3. Sub-100ms vLLM Inference Performance

Deploying fine-tuned SLMs on vLLM with PagedAttention enables concurrent batching, sub-100ms time-to-first-token (TTFT), and high-throughput production throughput.