Author here. Some context and the caveats up front. The model is Kimi K3, 2.78T parameters, ~1.45 TB of expert weights. It does not fit in memory, so the experts stream from disk: one 17.5 MB file per (layer, expert), read with pread + F_NOCACHE, 16 of 896 per layer. The machine is an M5 Max MacBook Pro with 128 GB and three Thunderbolt 5 enclosures plus the internal SSD. Expert weights are untouched at their released MXFP4 precision; the resident attention trunk is int8, which upstream labels non-weight-exact, so I don't claim bit-exactness against BF16 — I claim token-identical output against my own reference on the prompt of record, checked on every promotion. Numbers, with the unflattering ones in the same paragraph as the good ones: 1.00 tok/s steady over a 512-token completion, 1.13 over 128 tokens, and 0.96 median on the 17-token benchmark from the upstream repo's issue #15 against the 0.684 posted there. Time to first token on a 512-token prompt is about 6.3 minutes — prefill is currently read-amplified 6.2x, which is the biggest open problem in the repo and is described in the results directory. What I think is actually interesting isn't the number, it's that four of the gains came from defects in the read path that instrumentation found and I would never have guessed. The instruments are in a second repo, ARGODRIVE — a 10 ms per-device read monitor, a per-read barrier trace that records which drive served each expert and which one landed last in every pass, and a config assertion harness that refuses to record a benchmark unless the setting under test actually fired. They're deltafin-specific today. The four findings: • A constant capped the reader threads at 16 and bounded both the demand and prefetch pools with the same value. Separating them was +14%; demand queueing went from 70% of blocked time to 7.5%. • Splitting each hot expert's read across two replicas on two devices was +10% — after the same knob had measured negative six times on layouts where every expert had one home and there was nothing to split against. • The prefetch path had no balancer at all: it walked a fixed directory order and took the first hit, so on any replicated layout it dumped everything on one enclosure. Giving it least-expected-completion dispatch with in-flight counters shared with the demand path was +11% and turned every replicated layout I had previously measured as a loss into a win. • A recorded "law" that a given draft depth was worse turned out to have been measured against a drafter that no longer existed. Re-testing it was +8%. There's also a drive-count ladder in the repo — same layout, one to four drives: 57% / 78% / 92% / 100% of the four-drive decode rate. And a catalogue of about a thousand timed runs of things that did not work, with the numbers: RAM expert caches from 8 to 40 GB (-4% to -48%), striping a single copy (-7 to -25%), two drives sharing one Thunderbolt link (-11%), streaming the attention trunk from SSD (-60%), Metal's file-loading API (-19 to -22%). That catalogue is the part I expect to be most useful to other people. The engine is a fork of gavamedia/deltafin, which is MIT and did the hard part; I've told the author about all of this and the upstream-relevant fixes are going back as PRs. Two things I'd genuinely like help with: whether anyone has done expert-major prefill scheduling on an MoE (read each expert once per layer and run its kernel over all rows routed to it — it should take prefill from 6.2x amplification to about 1x), and whether the drive ladder reproduces on other hardware.

But why though? Cannot possibly be useful at such slow speeds, and costs a ton to perform that badly