Koosha ParidehpourSystems / products / infrastructure
← Back to work

ai-ml · research

phenotype-omlx

Apple Silicon inference research fork with Rust performance cores, multi-backend routing, and evaluation tooling for comparing local model variants.

RustMLXApple SiliconEvaluation

Engineering lens

The experiment boundary keeps upstream MLX functionality distinct from fork-added multi-backend routing, Rust performance cores, and evaluation paths.

Problem

Local inference research needs a fast iteration loop across model variants, backends, and evaluation workloads on Apple Silicon.

Context

phenotype-omlx extends upstream `jundot/omlx`; upstream functionality remains attributed to that project.

Architecture

The fork boundary adds multi-backend routing, Rust performance cores, and evaluation paths around an MLX-focused runtime.

Constraints

Memory bandwidth, quantization choices, concurrency, and backend differences shape what can be measured locally.

Key decisions

Rust isolates performance-sensitive paths while routing and evaluation keep experiments comparable across backends.

Verification

Repository evidence covers the fork/extension boundary and research tooling; no production-scale claim is made.

Current limitations

Speculative decoding, quantization, and concurrency work remain research dimensions whose support varies by backend and model.

Retrospective

The project keeps upstream credit explicit while using a focused fork to explore Apple-Silicon inference performance.

Fork/extension of jundot/omlx; upstream attribution retained.

Provenance & Attribution

Evidence source
Repository documentation and project history
Provenance
Fork/extension of jundot/omlx; upstream attribution retained.
Available in lens
engineering
View repository