Core AI is Apple's on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple's coreai-torch (LLMs: coreai.llm.export) into .aimodel bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol (apple-silicon-llm-bench, macOS 27 beta, 2026-06).

Nemotron 3.5 ASR Streaming 0.6B — Core AI

nvidia/nemotron-3.5-asr-streaming-0.6b (OpenMDW-1.1 — commercial use OK, 600M) converted to Apple Core AI .aimodel — the first STREAMING ASR in the zoo: live microphone transcription in 320 ms chunks, on-device, any audio length (no 30 s bucket).

40 locales in ONE checkpoint — the language is a one-hot graph input (ja-JP, en-US, zh-CN, … or auto for built-in language ID), switchable per session at run time.
Punctuation + capitalization built in.
Cache-aware streaming encoder: the FastConformer's attention KV sliding window (56 frames) and causal-conv left context are explicit graph I/O, so each 320 ms chunk is one static-shape inference — no re-encoding, constant latency forever.
Pure-RNNT decode (LSTM predictor + joint) driven by a tiny host greedy loop.

Graphs (per 320 ms chunk at lookahead 3)

mel chunk (25 frames first, then 32)   [host: preemphasis→STFT→slaney mel→log, NO normalization]
 1. stream_pre_first / stream_pre : mel + 3 conv caches → embeds[1,4,1024] + caches   (fp16, 9 MB)
 2. stream_conformer_a : x + neg_mask[1,1,4,60]
                         + k/v_cache[12,8,56,128] + conv_cache[12,1024,8]
                         → x + updated caches                     (layers 0-11, fp16, 605 MB)
 3. stream_conformer_b : x + one_hot[1,128] + neg_mask + caches
                         → enc_proj[1,4,640] + updated caches     (layers 12-23 + prompt fusion
                                                                   + projector, fp16, 615 MB)
 host greedy RNN-T over the 4 new frames:
 4. predict : token[1,1] i32 · h,c[2,1,640] → dec_out[1,640] · h',c'                   (fp32, 61 MB)
 5. joint   : dec_out + enc_frame[1,640] → token_logits[1,13088]                       (fp32, 34 MB)
    blank(13087) advances a frame; a token emits + steps the predictor; 10/frame cap

The conformer ships in TWO halves: a single 24-layer AOT bundle (2.4 GB resources.bin) fails to load on-device (instant POSIX-2 from the loader — bisected: identical topology at 1 and 12 layers loads fine), so each half stays ~1.1 GB compiled, for one extra ~1 ms call per chunk. Platform subtrees: macos/ (JIT .aimodel) and ios/ (the halves AOT-compiled to h18p.aimodelc — big graphs' on-device JIT aborts; iPhone 17 Pro / A19 Pro). ModelID.nemotronASRStreaming picks the right one automatically.

Gated token-exact end-to-end vs the HF streaming reference (99/99 on LibriSpeech, chunked use_cache=True oracle == offline), and again token-exact through Swift CoreAIKit (KitNemotronModel, packet-size-independent mel frontend). blank 13087 · vocab 13088 · max 10 symbols/frame · 16 kHz.

Use (CoreAIKit)

swift

let nemotron = try await KitNemotronModel(model: .nemotronASRStreaming)

// LIVE: feed mic packets as they arrive; the transcript grows while you speak.
let session = try nemotron.makeSession(language: "en-US")   // or "ja-JP", … or "auto"
for await packet in micPackets {                            // 16 kHz mono Float, any packet size
    let partial = try await session.feed(samples: packet)
}
let result = try await session.finish()

// OFFLINE: any-length clip through the same streaming pipeline.
let result = try await nemotron.transcribe(samples: pcm16kMono, language: "en-US")

Try it in the zoo's coreai-audio app (Transcribe tab → "Nemotron Streaming 0.6B" → Live).

Speed

	per 320 ms chunk (warm)	real-time factor
M4 Max (GPU)	~26 ms	0.08 (12× real-time)
iPhone 17 Pro (GPU, AOT)	~53 ms	0.167 (6.0× real-time)

Load: ~52 s the first time after install (one-time GPU specialization of the two AOT halves), ~4 s cached thereafter.

Streaming latency = the model's lookahead (320 ms at the shipped lookahead=3) + chunk compute. The checkpoint also supports lookahead 0/6/13 (80 ms – 1.12 s); those variants re-export with a parameter change in the conversion scripts.

Convert yourself

conversion/nemotron_asr/ — streaming oracle (gen_oracle_streaming.py, HF chunked use_cache=True), cache-explicit re-author + export (export_encoder_streaming.py), token-exact gates (gate_e2e_streaming.py, gate_mel_swift_streaming.py).

License

OpenMDW-1.1 (see LICENSE) — the upstream NVIDIA model's license; this conversion redistributes the weights unchanged in a different serialization.

More models in this format: Core AI Model Zoo — 75 models, each with the recipe that produced it.

Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.

mlboydaisuke/Nemotron-3.5-ASR-Streaming-CoreAI