Research
Sustainable AI needs more than clever engineering. It needs a flagship model worth deploying everywhere, compression that keeps improving, and privacy guarantees that are architectural — not contractual. Our research addresses all three.
Research Area 01
7B-quality intelligence in a 750 MB footprint. Fern runs fully offline on a smartphone, Apple Watch, or an ESP32 embedded chip — no cloud, no latency, full privacy.
By the numbers
Active research directions
Pushing compression further on next-generation model families without quality regression.
Formal quality guarantees for regulated domains such as medical and defence.
Extending our compression pipeline to protein, diffusion, and vision-language models.
Building compressibility into the model from the start rather than applying it post-hoc.
Research Area 02
Our compression technology already achieves up to 60× reduction with no measurable quality loss. But we're not done. Our research is advancing the frontier — higher ratios, broader model coverage, and compression techniques that are provably safe for regulated environments.
Active directions include architecture-aware pruning for next-generation model families, lossless compression for safety-critical applications, and compression-native training pipelines that build compressibility into the model from the start.
These improvements feed directly into our products: better compression means smaller on-device footprints, lower enterprise serving costs, and higher-quality compressed models in our open-source releases.
Research Area 03
Running AI on-device is the most powerful privacy guarantee there is — data never leaves the user's hardware, there are no inference logs, and there is no surface for data leakage. But privacy goes deeper than deployment topology.
We are researching differential privacy techniques for fine-tuning, secure aggregation for federated model updates, and confidential inference on shared infrastructure. The goal: AI that is private by construction, not just by policy.
Combined with Fern's on-device capabilities, this makes our models uniquely suited to healthcare, defence, legal, and any other domain where data sovereignty is non-negotiable.
Why on-device privacy matters
Sensitive data stays on the device — it is never transmitted, stored, or processed by a third party.
Compliant with HIPAA, GDPR, and air-gapped deployment requirements without contractual workarounds.
Inference runs entirely offline — no API keys, no connectivity requirement, no third-party exposure.
Collaborate
We're looking for hardware partners, academic collaborators, and engineers who want to work on the foundations of sustainable AI.