Why TCP and RoCE Are Slowing Down AI: John Ousterhout on the Homa Transport Protocol
Published September 29, 2026
At a glance
Views
102,535
Likes
3
Comments
0
Performance vs. channel
All-time outlier score
Engagement rate
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About
### What You’ll Learn Your GPUs are sitting idle, and the network is why. John Ousterhout, Stanford professor and creator of Tcl, explains why TCP, RDMA, and RoCE struggle with AI workloads and what the Homa transport protocol does differently. On this episode of The Edge Case, recorded at the AI Engineer World's Fair…
- Published
- September 29, 2026
- Made for kids
- No
More features coming
We're working on new analytics and tools. Stay tuned.
How Why TCP and RoCE Are Slowing Down AI: John Ousterhout on the Homa Transport Protocol is doing
It has 102K views in total. It ranks #12,668,996 among tracked YouTube videos by views.
Key facts about Why TCP and RoCE Are Slowing Down AI: John Ousterhout on the Homa Transport Protocol
- Total views
- 102K
- Total likes
- 3
- Total comments
- 0
- Published
- September 29, 2026
- Global rank by views
- #12,668,996
Frequently asked questions
How many views does Why TCP and RoCE Are Slowing Down AI: John Ousterhout on the Homa Transport Protocol have?
Why TCP and RoCE Are Slowing Down AI: John Ousterhout on the Homa Transport Protocol has 102,535 views, 3 likes, ranking #12,668,996 among tracked YouTube videos by views.
How accurate are these YouTube video analytics?
View, like, and comment counts come from YouTube's public API and refresh regularly. Growth and revenue figures are estimates built from tracked historical data, so they can differ slightly from what the creator sees privately in YouTube Studio.
What is the outlier score?
How this video's views compare to the channel's usual Shorts or long-form video. A 2x score means double the usual views for that channel, 0.5x means half.
What is engagement rate and how is it calculated?
Likes and comments per view, compared to this channel's typical engagement rate — shown as above or below what's usual for the channel.
How is the revenue estimate calculated?
It's a rough public estimate based on a $0.7–$2.0 CPM (revenue per 1,000 views) applied to view count — not actual creator earnings, which depend on ad rates, audience geography, and monetization settings YouTube doesn't expose publicly.
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