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
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%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.003% |
| 25th percentile | 0.022% |
| 50th percentile | 0.128% |
| 75th percentile | 0.604% |
| 90th percentile | 2.32% |
| 99th percentile | 83.4% |
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.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 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 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
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.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 56% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 72% 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.
- 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
- 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 🧵
- 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
- 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
- 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!
- 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
- 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
- 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
- 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
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
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.
Buy or sell Twitter (X) accounts - escrow-protected
PlayerSells is an escrow marketplace for Twitter (X) accounts. Every deal is protected, with no middleman risk.
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.