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NVIDIA AI engagement report

@NVIDIAAI - 348K followers on X

Measured over 32 original posts from a 30-day window, last computed on September 15, 2026.

Engagement

Middle of its size range
Per follower
0.06%
of 348K followers
Per impression
1.26%
16K views on a typical post
Reach
4.76%
of its followers see a post
Typical post
204
interactions (median)
Saved
0.111%
18 bookmarks on a typical post
Posting rate
2.27/day
active 73% of days
Peak time
16:00 UTC
Tuesday

A typical post picks up 204 interactions against 348K followers, an engagement rate of 0.06%. Measured over 32 original posts, its engagement rate beats 52% of 15,519 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 16K times each, and 1.26% of those impressions turn into an interaction. That is about 4.65% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.3 posts a day over the last 30 days, with activity on roughly 73% of days. Most posts go out around 16:00 UTC, and Tuesday is the busiest day of the week. Of the 32 posts sampled, 56% carry an image or video, 22% are part of a thread and 72% link out. The account's strongest tracked post pulled 1.2K interactions, about 6.0x its own typical post. Recurring topics include #eccv2026, #nvidiagtc, #cosmos.

Measured over 32 original posts from a 30-day window, last computed on September 15, 2026. Recurring tags: #eccv2026, #nvidiagtc, #cosmos.

Compared with accounts its own size

NVIDIA AI's engagement rate beats 52% of the tracked X accounts closest to it in follower count (15,519 accounts, accounts of similar size (decile 9 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.

On engagement per impression rather than per follower it beats 52% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.

Where this sits in the catalog

At 0.06%, NVIDIA AI sits above the 25th percentile of the 157,233 accounts in this comparison. That places it in the below the median band, which runs 0.022% to 0.128%.

p100.003%
p250.022%
p50 (median)0.128%
p750.604%
p902.32%
p9983.4%
Engagement rate as a share of followers, across the 157,233 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 26,048 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.003%
25th percentile0.022%
50th percentile0.128%
75th percentile0.604%
90th percentile2.32%
99th percentile83.4%

This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built

Posting timing

This account posts most often around 16:00 UTC, and Tuesday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 16:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 16:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%89K
01:00 UTC-2%90K
02:00 UTC-3%88K
03:00 UTC-4%94K
04:00 UTC-5%76K
05:00 UTC-4%75K
06:00 UTC-5%86K
07:00 UTC-5%93K
08:00 UTC-4%108K
09:00 UTC-4%124K
10:00 UTC-3%129K
11:00 UTC-3%141K
12:00 UTC-3%154K
13:00 UTC-3%167K
14:00 UTC-4%173K
15:00 UTC-2%176K
16:00 UTC-3%171K
17:00 UTC-3%159K
18:00 UTC-2%149K
19:00 UTC-2%141K
20:00 UTC-1%131K
21:00 UTC0%116K
22:00 UTC-2%100K
23:00 UTC-1%90K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Tuesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Tuesday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%393K
Monday+1%483K
Tuesday-2%520K
Wednesday-3%472K
Thursday-2%430K
Friday-3%447K
Saturday+2%393K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Formats this account uses

Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.

This account's posting mix compared with catalog-wide effects
FormatThis accountCatalog effect95% intervalAccounts behind it
Image or video56% of posts+111%+108% to +115%34K
Outbound link72% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 56% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
  • 72% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
  • Its average post runs 242 characters, which falls in the 180 - 280 characters band. Across the catalog, posts of 180 to 280 characters match the same accounts' other posts almost exactly.

These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.

Best tweets

  • Aug 19, 20266.0x their median

    We benchmarked 300+ NVIDIA verified skills to see how much they actually help agents on real tasks. Same task, same model, same setup. The only difference was whether the agent had the skill. Across the benchmarks, skills improved correctness by 41 points, effectiveness by 39, and efficiency by 35. SkillEvaluator is open source if you want to test your own skills before you ship them.

    1.1K1223226305K viewsView on X
  • Sep 5, 20263.8x their median

    Congrats to our researchers for exceeding the gold medal threshold on the International Olympiad in Informatics (IOI) 2026 problem set 🥇 Our fine-tuned Nemotron model scored 535.4 out of 600, as graded by the IOI team — higher than the top-scoring human participant. The team competed unofficially in Uzbekistan, where the International Technical Committee supervised the human contestants. The model had no internet access and faced the same time limits and submission constraints, using the same platform in parallel with the official competition. Read more in the technical report: https://t.co/4paFvrIV2B

    60585492761K viewsView on X
  • Sep 1, 20263.7x their median

    Can a security defense catch an attack it hasn’t seen before? We teamed with @CrowdStrike to evaluate an offensive-defensive system built on its SafeMind agentic system, where AI agents simulate controlled attacks, turn telemetry into detection rules, then test them against new attack paths. Here’s how it works 🧵

    60890372071K viewsView on X
  • Aug 25, 20263.7x their median

    When an LLM engine crashes, a cold restart can mean minutes of lost capacity. Shadow engine recovery, a new preview feature in NVIDIA Dynamo, keeps a standby engine warmed up and ready to take over. In our GLM-5.2 test, it restored capacity in 7.3 seconds, nearly 39x faster than a cold restart. Read all about it: https://t.co/dUVzW731vc

    64853371563K viewsView on X
  • Aug 18, 20263.2x their median

    We just released TensorRT Model Connect in Public Preview. You can take a supported @huggingface model to end-to-end TensorRT inference in just two commands. No intermediate ONNX export, and the resulting bundle can run through native C++ APIs. We also built the entire project with @OpenAIDevs Codex agents, with humans directing and reviewing the work. That includes model implementations, performance tuning, tests, integrations, and docs. It’s open source, so go try it out, dig into the implementations, or contribute support for a new model: https://t.co/qlOT90ylxY

    54368331698K viewsView on X
  • Aug 28, 20262.8x their median

    10 million downloads for NVIDIA Warp 🎉 Warp started with a simple idea: you shouldn’t have to leave Python to get real GPU performance for physics and simulation. Since then, developers have used it to accelerate work across physics simulation, computational engineering, geometry processing and robotics. Thank you to everyone who downloaded it, broke it, filed an issue or sent a PR. On to the next 10M!

    4876323827K viewsView on X
  • Sep 4, 20262.6x their median

    Need faster LLM inference without sacrificing accuracy? Speculative decoding can help. Choosing the right draft length and drafting method depends on your model, workload and hardware. We break down five practical guidelines for balancing throughput and latency. https://t.co/k6yqH8p9ig

    43651321036K viewsView on X
  • Aug 12, 20262.2x their median

    Love seeing our partners post-training Nemotron 3.5 Lightning for their own domains, tools, and workflows, as well as those offering post-training support ⚡ Check out below to see what they’re building 🧵 https://t.co/hgi7dskM8A

    3654421930K viewsView on X
  • Aug 28, 20262.1x their median

    Already running an inference engine? So where does NVIDIA Dynamo fit in? In five minutes, we break down how Dynamo sits around engines like @sgl_project, @vllm_project and TensorRT-LLM to scale inference across GPUs and nodes. Full video in the comments 🔽 https://t.co/BA093Ab3gJ

    3254152831K viewsView on X
  • Sep 9, 20261.8x their median

    We're introducing Q2D-Web (Query2Doc-Web), a benchmark and public leaderboard for evaluating retrieval in agentic RAG systems. Q2D-Web tests how embedding models perform on large-scale web search using agent-reformulated search queries. Read more: https://t.co/s476SxkE1L

    28542281191K viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

Recurring topics

#eccv2026#nvidiagtc#cosmos#ibc2026

The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.

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Reading these numbers

A typical post picks up 204 interactions against 348K followers, an engagement rate of 0.06%. Measured over 32 original posts, its engagement rate beats 52% of 15,519 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 16K times each, and 1.26% of those impressions turn into an interaction. That is about 4.65% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.3 posts a day over the last 30 days, with activity on roughly 73% of days. Most posts go out around 16:00 UTC, and Tuesday is the busiest day of the week. Of the 32 posts sampled, 56% carry an image or video, 22% are part of a thread and 72% link out. The account's strongest tracked post pulled 1.2K interactions, about 6.0x its own typical post. Recurring topics include #eccv2026, #nvidiagtc, #cosmos.

What is NVIDIA AI's engagement rate on X?
NVIDIA AI (@NVIDIAAI) has an engagement rate of 0.06%, based on the median interactions across 32 original posts from the last 30 days against 347,524 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.06%, NVIDIA AI sits above the 25th percentile of the 157,233 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
Does @NVIDIAAI have real engagement?
Its engagement rate beats 52% of the tracked X accounts closest to it in follower count (15,519 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
When does @NVIDIAAI post?
Most posts go out around 16:00 UTC, and Tuesday is its busiest day, at roughly 2.27 posts per day across the measured window.

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