Matryoshka LM

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: Matryoshka LM
Matryoshka Language Model — 36% Compute Savings

Core idea: One training run produces a *nested family* of LLMs (small, medium, large). At inference, queries are routed to the smallest model that can handle them — achieving 36% compute savings with no measurable quality loss on representative workloads. Applicable to: Any production LLM serving system — inference APIs, RAG rerankers, chat orchestration, code completion endpoints.

## Reference implementation (Vector Ridge Labs, POC-022)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-022
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/matryoshka_lm

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures latency, throughput and cost per request at equal output quality, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.