Overview
Running large frontier models in production is expensive and slow. Model distillation transfers the capability of a large teacher model into a smaller, faster student model — preserving performance on your specific task while dramatically reducing inference cost and latency. We design distillation programmes that produce compact models suitable for high-volume production workloads, edge deployment, or environments where data residency requirements prevent use of external APIs. The result is AI that performs like a frontier model but runs like a lightweight service.
How It Works with a21
Capability Assessment & Target Setting
Define the task scope and performance targets. Assess which capabilities of the teacher model are essential — and which can be traded against cost and speed.
Data Generation & Distillation Training
Use the teacher model to generate training data at scale for the student. Train the student model using knowledge distillation techniques, with iterative evaluation against performance targets.
Validation & Production Deployment
Validate student model performance against the teacher on held-out test sets. Deploy to production with latency and cost benchmarking to confirm the business case.
What We Offer
Teacher-Student Distillation
Transfer capability from frontier models (GPT-4o, Claude) to compact student models — preserving task-specific performance while reducing inference cost by 70–90%.
Synthetic Data Generation
Use teacher models to generate high-quality synthetic training data at scale, enabling distillation even when labelled examples are scarce.
Task-Specific Compression
Optimise distillation for your specific task — classification, extraction, summarisation — rather than general-purpose compression.
Quantisation & Optimisation
Apply quantisation (INT8, INT4) and pruning techniques to further reduce model size and inference cost post-distillation.
Edge & On-Premise Deployment
Package distilled models for edge deployment or on-premise inference — enabling AI in environments where cloud connectivity or data residency is a constraint.
Cost & Latency Benchmarking
Provide detailed before/after benchmarking of inference cost, latency (P50/P95/P99), and performance — so the business case for distillation is quantified.
Why Choose a21
Rigorous Performance Validation
We do not ship distilled models that do not meet the performance bar. Every distillation project has defined acceptance criteria tested before deployment.
Cost Engineering Mindset
We optimise for the full cost profile — not just model size, but tokenisation efficiency, batching strategy, and caching — to maximise inference economics.
Data Residency Compliant
Distilled models run in your environment. For organisations with strict data residency requirements, distillation eliminates dependence on external APIs.
Production Proven
Our distilled models serve millions of production inferences. We design for reliability, latency consistency, and graceful degradation under load.
Success Stories
Problem
A financial services firm was classifying 2 million documents per month using GPT-4 — with an annual inference cost exceeding £800K and latency averaging 4 seconds per document.
Solution
Distilled a task-specific classifier from GPT-4 outputs into a fine-tuned Llama 3 8B model, with quantisation for production deployment.
Problem
A medical device company needed AI-powered clinical decision support running on-device in hospital environments with no internet connectivity.
Solution
Distilled and quantised a clinical knowledge model from a frontier LLM, optimised for deployment on CPU-only hardware at the bedside.
Tech Stack & Tools
Hugging Face Transformers
PEFT / LoRA
bitsandbytes
llama.cpp / ONNX Runtime
vLLM / TGI
W&B
Get Started
Reduce your AI inference costs without sacrificing performance. Talk to a21 about model distillation.
