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thunderbolt-ibverbs: We have InfiniBand at home

▲ 125 points • 19 comments • by zdw • 5mo ago • HN discussion ↗

Pangram verdict · v3.3

We believe that this document is fully AI-generated

99 %

AI likelihood · overall

AI
0% human-written 100% AI-generated
SEGMENTS · HUMAN 0 of 1
SEGMENTS · AI 1 of 1
WORD COUNT 239
PEAK AI % 99% · §1
Analyzed
Jun 4
backend: pangram/v3.3
Segments scanned
1 windows
avg 239 words each
Distribution
0 / 100%
human / AI fraction
Verdict
AI
Pangram v3.3

Article text · 239 words · 1 segments analyzed

Human AI-generated
§1 AI · 99%

I spent the past few weeks building a linux kernel module that makes ordinary USB4/Thunderbolt ports on AMD mini PCs pretend to be InfiniBand devices. The goal is simple: let existing AI runtimes like vLLM/RCCL split inference or training across multiple boxes at home, without buying enterprise networking gear. TL;DR. We built experimental RDMA-over-USB4 for 128GB Strix Halo mini PCs. It lets two consumer boxes talk fast enough to run tensor-parallel inference and FSDP workloads across both machines: ~95 Gb/s bidirectional raw RDMA, ~7 µs one-way latency, a MiniMax-M2.7 TP=2 inference run that does not fit on one box, and a Gemma 3 27B LoRA FSDP step falling from 1359 s over Ethernet to 126 s over 4-HCA USB4 RDMA.

~48 Gb/s per direction (~95 Gb/s bidi total) sustained ib_write_bw, 4-HCA aggregate at 1 MiB / 8 QPs with IOMMU off — vs ~2.3 Gb/s over the onboard 2.5 GbE and ~9 Gb/s for soft-RoCE on top of thunderbolt-net at the per-rail level. ~7 µs one-way ib_write_lat at 64 B, single QP — vs ~28 µs over RXE/2.5 GbE and ~65 µs over RXE/TBnet.

DISCLAIMER: this is research code, most of it AI-generated, and it loads experimental kernel modules on machines I was willing to crash repeatedly. I made an effort to understand enough of it to keep it on-track, but there are almost certainly false assumptions and sharp edges throughout. No warranty, no support promise, not production software.