Colibri explained: streaming MoE experts across storage, RAM and VRAM
Operating Colibri safely: model provenance and inference exposure
Keep downloaded artifacts, network access and resource budgets within an explicit boundary.
What you will learn
- Review both code and weights
- Verify the serving boundary
- Plan resource and incident recovery
Before you start
- Basic Python and command-line knowledge
- A documented hardware and storage inventory
Create a reproducibility ledger before attempting another inference optimization.
Key takeaways
- Local computation still has supply-chain risk.
- Serving security is a separate review.
- Recovery should not silently change the comparison.
Review both code and weights
An open engine license does not make every model artifact trusted. Record source, revision, format and license before preparing weights. Review scripts and dependencies before running conversion or installation.
Use a dedicated directory and retain the original manifest. A large download or conversion can exhaust disk capacity; reserve operating-system space and avoid running experiments with unrelated privileged credentials.
Verify the serving boundary
Local inference does not automatically mean every surrounding service is private. The advertised web or API surface still needs listener, authentication, logging and data-retention checks before remote use.
The relevant gateway enforcement paths were not audited here. Keep a trial local and restricted until those checks are complete, and use synthetic prompts instead of customer data during initial diagnostics.
Plan resource and incident recovery
Limit concurrent trials and watch memory, free disk and failed requests. Preserve redacted logs when a run fails; repeated restarts can hide the first useful error or trigger repeated preparation work.
If an artifact’s provenance is uncertain, stop using it and retain its identifiers for review. Restore a previously reviewed engine/model pair instead of silently switching quantization and comparing the outputs as the same model.
Decision guide
| Criterion | Option A | Option B |
|---|---|---|
| Best when | You need predictable behavior and easy auditing | You need adaptive optimization and have reliable telemetry |
| Main risk | May leave performance on the table | Can become difficult to explain or debug |
Implementation steps
- 1
Record code and model provenance separately.
- 2
Verify serving access before exposure.
- 3
Preserve errors and restore a reviewed pair.
Copy-ready example
trial_boundary:
prompts: synthetic
network_exposure: restricted
model_manifest: recorded
free_disk_reserve: required
recovery: reviewed-engine-model-pairFrequently asked questions
Does running locally remove all privacy concerns?
No. Gateways, logs, downloads and connected clients still need review.
Is gateway authentication proven by this series?
No. That enforcement path remains outside the inspected scope.
Sources
- Colibri / docs/quickstart.mdSource checked 2026-09-23
- Colibri / README.mdSource checked 2026-09-23
- Colibri / docs/benchmarking.mdSource checked 2026-09-23