LLM Distillation Services for Your Business
LLM distillation services train a small model to copy how a large one handles a specific task. Omeiro uses outputs from models such as Claude and GPT-4o to build synthetic examples, then trains a compact 8B model that is cheaper to host.
How LLM distillation works, step by step
A made-up example of one run, from start to finish. Each step is logged so you can see what happened.
- Something happens
A form, an email, a payment or a new row.
- The workflow runs
Steps you agreed, in the right order.
- Errors are caught
Failed steps retry or alert a person.
- You see what ran
A simple log of every run.
Sample Made-up names, data and steps to show how a run is logged. Your steps, tools and rules follow your business.
What you get with LLM distillation
Knowledge distillation pipelines training compact 8B models on synthetic outputs from Claude 3.5 Sonnet and GPT-4o. Aiming for frontier-level reasoning at a far lower hosting cost.
Basic Package
Distill 1 task into an 8B model with 3k synthetic examples. 8-day delivery, 1 round + 30-day fixes.
Standard Package
Up to 3 tasks, 15k examples, eval vs teacher model, quantized build. 14-day delivery, 1 round + 30-day fixes.
Premium Package
Up to 6 tasks, 50k examples, serving setup, cost report, 30 days support. 21-day delivery, 1 round + 60-day fixes.
Clear brief first
We confirm what you need before work starts, so the scope is clear on both sides.
Revisions listed
Each package shows its revision rounds. A round is one list of changes sent together.
Yours once paid
Copyright of the final work passes to you once the order is paid in full.
Third-party tools, API usage and hosting are billed to your own accounts, so the setup stays with your business.
How our LLM distillation works
Choose the task
We pick a narrow job where a small model can succeed.
Generate examples
A larger model produces inputs and answers that are filtered for quality.
Train the student
The 8B model is fine-tuned on those examples.
Compare and serve
Results are measured against the teacher and, on Premium, deployed.
LLM distillation: choosing a task a small model can learn
Distillation works best on narrow tasks with clear right answers. Classifying tickets, extracting fields from documents or answering questions inside a fixed domain suit a small model, while open-ended reasoning across many topics usually does not. Before ordering LLM distillation, define the task precisely, collect real inputs and decide what counts as a correct output. The tighter that definition, the more the synthetic examples can teach and the easier the comparison with the teacher becomes.
Data quality matters more than volume. The examples a teacher model produces contain mistakes, so a filtering step checks them before training, and a held-out set measures the student fairly. LLM distillation also changes your costs: you trade per-token fees for hosting and maintenance, so estimate your monthly request volume. Check the teacher provider's terms on training other models with its outputs, and tell us the latency target and hardware you plan to serve on.
In short
- Delivery in 8 to 21 days, by package.
- Fixed price, shown before you order.
- Teams paying large model prices for one narrow, repetitive task.
- Startups wanting to distill gpt into smaller model hosting they can control.
- Copyright yours after full payment.
LLM Distillation: common questions
How much does LLM distillation cost?
Prices are fixed and shown by package on our LLM Distillation pricing page, so you see the full price before you order. You can also ask for a custom quote.
How long does model distillation consulting take?
Delivery depends on the package: Basic 8-day delivery; Standard 14-day delivery; Premium 21-day delivery.
What is included in the Basic Package?
The Basic Package includes: Distill 1 task into an 8B model with 3k synthetic examples. Higher packages add more scope, revisions and support.
Can I get a custom small language model training package?
Yes. If none of the packages fits, use the custom package form on our pricing page to describe what you need, and our team sends you a fixed price.
Will the small model match the big one?
Usually only on the narrow task it was trained for, which is why Standard compares it with the teacher.
Do the large model's terms allow this?
Providers' terms differ, so check yours. We can discuss it before ordering.
Do I need my own GPUs?
Copyright of the final work passes to you once the order is paid in full. Paid fonts, stock files or licences, if used, are licensed in your name.
What size is the model?
An 8B model.
What is the difference between distillation and fine-tuning?
Fine-tuning trains a model on examples you already have. Distillation creates those examples from a stronger teacher model, so a small model learns to imitate it on a chosen task.
Ready to start your LLM distillation?
Compare the packages and pick the one that fits. Not sure which? Write to us and we will help you choose.