A native sparse-active architecture program by Gamma AI. Designed for efficient reasoning, multilingual intelligence, and verified model evolution in one consolidated system.
Gamma AI is building two connected model lines: a compact sparse-active release first, and a larger frontier-class Genesis line after the engineering loop is proven.
The first release line: a 30B-class sparse-active system targeting roughly A6B balanced active compute, designed around Gamma routing, EvoStream iteration, verifier-first training, multilingual capability, and strict no-regress validation.
The frontier line: a Genesis-native 100B+ model designed to scale the same principles: sparse-active capacity, AXB compute control, hybrid long-context memory, rigorous evaluation, and cloud-native training operations.
Gamma Genesis Native is Gamma AI's architecture line: hybrid sequence memory, sparse active compute, task-driven routing, AXB active-budget control, MTP acceleration, and verifier-first training.
The product direction is intentionally simple: one consolidated model, strong reasoning, efficient inference, transparent validation, and documentation that can be defended.
Large total capacity, low active compute, Gamma-native routing, 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.
EvoStream is our disciplined improvement loop: plan, train, verify, compare, and promote only when measurable gains survive no-regress gates.
We publish engineering states that have passed internal checks. Public benchmark claims wait for complete peer runs.
One consolidated release is the goal. Gamma AI is not building a public-facing router disguised as a model.
Every improvement must pass verification, safety, multilingual, and no-regress checks before promotion.
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 validation 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.
Scale the proven EvoStream workflow to the Gamma 100B+ line.
