Feb 2026
Unified Memory Options
Apple M3 Ultra vs NVIDIA Grace-Blackwell on CPU-GPU data movement and the business tradeoffs of unified memory.
Verifiable, offline AI systems.
We focus on traceable model runs and offline deployment.
Clear provenance, documented configs, and repeatable setups.
Design goals
Offline by design
Designed to run without outbound network calls or telemetry.
Repeatable runs
We aim for reproducible setups with documented tolerances and signed artifacts.
Data minimization
We design for minimal collection and keep evidence local where possible.
Verifiable is not truth
We do not promise the model is right. We aim to show what model ran, with what config, on what input. That's the part you can verify.
Provenance
Artifacts should trace back to their origin. Model weights, adapters, and runtime are identified where possible.
Manifests
Structured declarations of what should run. Machine-readable. Diffable.
Signed Configs
Configurations can be signed so tampering is detectable.
Hash-Chained Logs
Log entries can reference the previous; deletion or modification becomes detectable.
Energy is a constraint
In transfer-heavy workloads, data movement dominates energy cost. Unified memory architectures can reduce this cost by eliminating copies between CPU and GPU memory. We measure this with Joules per token.
Methodology
We document a measurement methodology for Joules/token benchmarking on Apple silicon.
macOS powermetrics API • 10-run averaging • thermal normalization • documented tolerances
Recent Research Notes
View all →Jan 2026
Compliance Roadmap
Target frameworks and current status for CMMC, AS9100, and ITAR compliance.
Jan 2026
MLNavigator and adapterOS
MLNavigator is the company. adapterOS is the deterministic inference runtime it commercializes.
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