A native sparse-active architecture for verified model evolution. Built to become one consolidated model, not a permanent router or a loose set of experts.
Gamma AI is being built as two connected releases: a compact sparse-active model first, and a larger frontier-class Genesis line after the engineering loop is proven.
The near-term engineering model: a 30B-class sparse-active system targeting roughly A6B balanced active compute, built through the current Nemotron 3 Nano scaffold, EvoStream chromosomes, verifier-first data, and strict no-regress gates.
The larger consolidation target: a Genesis-native 100B+ model informed by the same chromosome ledger discipline, with Nemotron, GPT-OSS, DeepSeek, Qwen, MiniMax and other selected parents contributing verified architecture, behavior, trajectory, safety, and domain genetics.
Gamma Genesis Native is the intended architecture line for Gamma AI: a model family designed around hybrid sequence memory, sparse active compute, task-driven routing, and verified chromosome consolidation.
The current practical line uses Nemotron 3 Nano as the engineering scaffold for Gamma 30B-A6B. Genesis Native is the next target: an owned scaffold with Gamma routing, Gamma training gates, and Gamma model identity.
Large total capacity, low active compute, measured transfer from parent models, and strict acceptance gates.
Global attention keeps exact binding and tool structure. The SSM track is the long-context memory path that should reduce cache pressure and improve sequence efficiency.
Latent expert organization handles capacity. Task-driven routing selects cognitive modes such as reasoning, code, safety, memory, math, multilingual work, and tool use.
One checkpoint should adapt its active compute to hardware: eco near 10%, balanced near 20%, quality near 30%. The first public target is the 30B-A6B class.
Parent models contribute through traceable artifacts: adapters, verified behavior, trajectories, safety data, reward labels, architecture objectives, or proven compatible deltas.
The site reports what has been verified, what is prepared, and what remains a target. No benchmark claims are made before full peer runs.
One model/release is the goal. Temporary teachers, swarms, adapters, and specialists are allowed only as genetic extraction tools.
A contribution is not claimed until its chromosome artifact exists, passes acceptance checks, and appears in the ledger.
Every paid GPU cycle starts with a plan, writes logs, uploads durable artifacts, destroys the instance, and updates project status.
The next work is engineering, not narration: finish the repair loop, harden the release candidate, then run fair peer benchmarks.
Run G1-359 continuation and G1-360 strict validation on cloud GPU.
Analyze deltas, weak rows, multilingual coverage, and safety behavior.
Package the strongest Gamma 30B-A6B release candidate with provenance.
Benchmark against same-class peers before any public performance claim.
Transfer the proven EvoStream workflow to the Gamma 100B+ line.
