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The Real-World Problem

Large frontier models already know how to do your task, but running them in production can be painfully slow and expensive.

Distillation fixes that.

StepWhat HappensWhy You Care
1. Fine-tune a teacher (pick any strong open-source or proprietary checkpoint).We make the model excellent on your task, no compromises.Accurate.
2. Distill that teacher into a smaller student (3-12 B).The student learns by copying the teacher’s answers.5-10× faster & cheaper.
3. Deploy the student behind the same /chat/completions endpoint.Zero code changes besides the model name.Straight to prod.

You don’t need labeled outputs.
Provide a pile of inputs (dog images, video frames, customer emails).
The teacher generates the supervision signal; the student learns from it.

It’s possible you already have users passing in data to a large, overly expensive LLM. We can just use that data to fine-tune the model!

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When to Reach for Distillation

Use-caseWhy Distill?
Image or video taggingTeacher handles vision; student runs on a single GPU for nightly batches.
High-volume classification10 K requests/sec without burning $$.
Strict JSON / XML extractionStudent stays inside schema; latency drops below 100 ms.
Edge deploymentFit a 7 B model on CPUs or mobile, no external API call.

Rule of thumb: If the teacher nails quality but misses your latency or cost SLO, distill it.

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What Distillation Looks Like

Here’s model an example of a model training run where we distilled a 27B model into a 12B model. Notice that the distilled model was effectively able to learn the same task as the teacher model, but at a fraction of the cost and latency.

We first fine-tuned the teacher model on a set of 100k examples passed through Gemini-2.5-Pro. Then we distilled the teacher model into a 12B model. The distilled model was able to learn the same task as the teacher model, but at a fraction of the cost and latency!

Gemma 12 B distilled accuracy chart

  • Gemma 27 B teacher (fine-tuned) vs Gemma 12 B student
  • ∼90 % token accuracy (student) at ~4× speed and 1/3 memory
  • Runs on a single A100 instead of eight H200s

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Fine-Tuning Techniques We Use (and Why)

TechniqueWhen We Recommend It
Full fine-tuneMaximum quality; you own the weights.
LoRA / AdaptersSmall dataset, rapid iteration, weights ≤ 1 GB.
Quantised LoRAEdge devices, CPU inference.
DistillationProduction latency/cost constraints.

Our team has shipped hundreds of custom models. Email [email protected] and we’ll pick the right recipe for you.

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What Happens Next

  1. Scoping call – goals, latency SLO, data hand‑off.
  2. Our experts fine-tune and deploy your custom model.
  3. Validation report – accuracy, latency, cost projections.
  4. Production endpoint – isolated, autoscaled, monitored.

Need results by next week? We’ve done it before. Ping us—let’s build something that actually ships.