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Compare the fully loaded economics of distilling and fine-tuning specialized small models (Qwen, Llama, Mistral on vLLM/Serverless GPU) vs. paying per-token to frontier APIs (OpenAI, Anthropic, Google). Calculate team capacity gains and payback timelines.
Ideal for JSON extraction, text classification, routing, and domain NER
I help teams identify automatable task boundaries, synthesize distillation datasets, and deploy production-hardened vLLM clusters with continuous eval benchmarks.
Book an Inference Economics AuditFrontier models (like Claude 3.7 Sonnet or GPT-4o) charge $2.50–$15.00+ per million tokens because they maintain general-purpose capabilities. By distilling the specific task (e.g. classification, legal extraction, JSON formatting) into a 7B–14B open-weight model (Qwen 2.5, Llama 3.1), inference costs drop to $0.05–$0.20 per million tokens (or flat GPU hosting costs). For medium-to-high volume workloads (>100k queries/month), this routinely produces 75%–92% annual infrastructure savings.
We analyze your production query distribution and isolate well-bounded, high-volume tasks. We use a frontier model (the "teacher") to generate synthetic reasoning paths and gold-standard supervision, then train and align a compact open-weight model (the "student") using LoRA / full fine-tuning. Production deployment on vLLM provides sub-100ms latency and 100% data sovereignty inside your own cloud or on-premise infrastructure.
Our calculator uses real GPU pricing (vLLM on L4/A100 instances and serverless providers) alongside industry benchmarks from enterprise AI deployments. The projections account for a hybrid routing architecture where 80–90% of routine traffic is handled by the fine-tuned small model and complex edge cases fall back to frontier APIs.
The distillation tier estimates include task boundary audit, dataset curation & synthetic generation, LoRA / DPO alignment, automated evaluation benchmark suites, quantization, and deployment to private vLLM clusters with continuous latency monitoring.