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Decision 3.0 now on Core ML 🍎 d3-lite triaging 1,000 pull requests into vllm-sr workgroups, live on an M5 Pro: 0.12 s per PR, 8.3 PRs/s, nothing leaves the Mac ⚡ lite, nano and mini are all converted and in one repo 👇 🤗 🛠️

28,713 Aufrufe • vor 1 Monat •via X (Twitter)

15 Kommentare

Profilbild von basedcapital
basedcapitalvor 1 Monat

a thousand prs triaged at 8.3 a second on a mac, by a model built for routing decisions. the boring infra jobs are where small models win

Profilbild von Wallet Watcher
Wallet Watchervor 1 Monat

d3-lite at 0.12s per PR is the number that matters. everything local, nothing leaves the Mac. that's the part people skip when they benchmark against API calls.

Profilbild von Rajan Singh
Rajan Singhvor 1 Monat

Great work man

Profilbild von NYC Arb Scanner
NYC Arb Scannervor 1 Monat

0.12s per PR on-device, nothing leaving the Mac. that's the number I care about — local cadence with no network tail is the whole point.

Profilbild von Julian · Media AI
Julian · Media AIvor 1 Monat

A 0.6B model that watches video and just picks an answer is the tiny intern of my dreams 🎬

Profilbild von Darui
Daruivor 1 Monat

When a PR is ambiguous between workgroups, can the router leave it in a human-review queue, or does it always have to pick one?

Profilbild von Alex
Alexvor 1 Monat

in general decision models should have that option but for this demo the pre existing sections is suffice for demoing.

Profilbild von Muhamed Fazal PS
Muhamed Fazal PSvor 1 Monat

sub-120ms per pr locally on core ml without hitting cloud tokens is clean. are you running direct ane fp16 execution for the embedding classification passes, or falling back to gpu metal shaders on the larger token context windows?

Profilbild von Alex
Alexvor 1 Monat

All GPU: Core ML on the Metal GPU, int8 weights with fp16 compute, padded to a few fixed prompt lengths. d3's hybrid DeltaNet layers don't compile for the Neural Engine yet, so Core ML falls back to CPU there and the GPU is the fast path. 0.12 s per PR = 2 questions on d3-lite, ~50 ms each.

Profilbild von Muhamed Fazal PS
Muhamed Fazal PSvor 1 Monat

metal gpu with fp16 accumulators avoids the neural engine dynamic shape penalty anyway. fixed prompt bucket padding is the move. stops coreml graph recompilation on variable token lengths.

Profilbild von BreezeOg
BreezeOgvor 1 Monat

0.12s per pr on a laptop and nothing leaves the mac, that's the part i like

Profilbild von T.Rex-Baby
T.Rex-Babyvor 1 Monat

Not bad, still a long way to go for full usage. Fast but not fast enough for heavy continuous work.

Profilbild von icreate
icreatevor 1 Monat

@grok any jev benchmark on this one?

Profilbild von Felipe Daragon
Felipe Daragonvor 1 Monat

how is D3 comparing to Laya?

Profilbild von Alex
Alexvor 1 Monat

i think they serve different purposes. i haven't fully explored laya but d3 does seem to offer way more options in terms of tasks, quality and inference options

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