Strata vs uzu
Hybrid GPU/CPU MoE engine that runs Qwen3.8-Flash-Next and DeepSeek V4.1 on consumer cards: hot experts cached in VRAM, rest computed in RAM, MTP drafting, local OpenAI/Anthropic API. — versus — Rust inference engine for on-device AI on Apple Silicon (Metal, unified memory) with Swift, Python and TS bindings, OpenAI-compatible server and a TypeSafe System One decision-model API.
Both run models locally behind OpenAI-compatible endpoints: Strata serves huge MoEs on a gaming GPU, uzu embeds small models inside Apple apps.
| Strata | uzu | |
|---|---|---|
| Stars | 41k | 3.2k |
| Forks | 4.4k | 221 |
| Language | C++ | Rust |
| License | MIT | MIT |
| Last activity | yesterday | yesterday |
| Topics | local, coding | local, decision-models |
| Curated connections | 5 | 5 |
Strata — the curator's take
Reach for it when you own a 12-24 GB GPU (NVIDIA, AMD, Apple Silicon) and want a frontier-class MoE behind Claude Code, Codex or OpenCode with no cloud — one installer, `strata connect` wires the tools. It is a purpose-built engine for a handful of model families, not a general runner: for arbitrary GGUFs or multi-user serving use Ollama/vLLM, and on low RAM or small disks the DeepSeek packs (200+ GB) will not fit.
uzu — the curator's take
Pick it when you are shipping an iOS/macOS app and want a local LLM embedded in-process — Swift Package, structured output, tool calls, and a cloud fallback through the same API. Not for NVIDIA/Linux serving (Metal and CPU only) or bring-your-own GGUF: models need its own format, converted with lalamo.