PyTorch engagement report
@PyTorch - 509K followers on X
Measured over 46 original posts from a 30-day window, last computed on August 28, 2026.
Engagement
A typical post picks up 32 interactions against 509K followers, an engagement rate of 0.006%. Measured over 46 original posts, its engagement rate beats 25% of 3,774 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 0.206% of those impressions turn into an interaction. That is about 3.10% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.8 posts a day over the last 30 days, with activity on roughly 60% of days. Most posts go out around 15:00 UTC, and Thursday is the busiest day of the week. Of the 46 posts sampled, 93% carry an image or video and 96% link out. The account's strongest tracked post pulled 198 interactions, about 6.2x its own typical post. Recurring topics include #pytorchcon, #ai, #cloudnativecon.
Measured over 46 original posts from a 30-day window, last computed on August 28, 2026. Recurring tags: #pytorchcon, #ai, #cloudnativecon.
Compared with accounts its own size
PyTorch's engagement rate beats 25% of the tracked X accounts closest to it in follower count (3,774 accounts, accounts of similar size (decile 7 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 17% 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 0.006%, PyTorch sits above the 10th percentile of the 36,521 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.012%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.08% |
| 75th percentile | 0.434% |
| 90th percentile | 2.10% |
| 99th percentile | 160.7% |
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 15:00 UTC, and Thursday 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% | 50K |
| 01:00 UTC | -2% | 51K |
| 02:00 UTC | -3% | 50K |
| 03:00 UTC | -4% | 53K |
| 04:00 UTC | -6% | 43K |
| 05:00 UTC | -4% | 42K |
| 06:00 UTC | -4% | 48K |
| 07:00 UTC | -5% | 52K |
| 08:00 UTC | -4% | 60K |
| 09:00 UTC | -3% | 69K |
| 10:00 UTC | -2% | 72K |
| 11:00 UTC | -3% | 78K |
| 12:00 UTC | -2% | 86K |
| 13:00 UTC | -2% | 94K |
| 14:00 UTC | -4% | 97K |
| 15:00 UTC | -2% | 100K |
| 16:00 UTC | -3% | 97K |
| 17:00 UTC | -2% | 90K |
| 18:00 UTC | -1% | 84K |
| 19:00 UTC | -2% | 79K |
| 20:00 UTC | -1% | 74K |
| 21:00 UTC | -1% | 66K |
| 22:00 UTC | -2% | 57K |
| 23:00 UTC | -2% | 51K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +4% | 230K |
| Monday | 0% | 286K |
| Tuesday | -2% | 276K |
| Wednesday | -1% | 251K |
| Thursday | -1% | 244K |
| Friday | -3% | 252K |
| Saturday | +3% | 227K |
Best tweets
- Aug 10, 20266.2x their median
Today @AIatMeta introduced Muse Glimmer, an open-weight, 30-billion-parameter model distilled from Meta’s Muse Spark for on-device agentic workflows. Alongside, ExecuTorch is adding end-to-end support for running Muse Glimmer on @NVIDIA GPUs and Macs with @Apple silicon. Why ExecuTorch? ExecuTorch offers a smarter, native approach: ⚡ Build in PyTorch: Implement your model and decoding strategies right in PyTorch as you normally would ⚡ Seamless Export: Export directly to ExecuTorch when you're ready for deployment ⚡ Automated Optimization: The framework automatically handles backend-specific lowering (Triton on CUDA, MLX-native, or Metal on Apple silicon) and uses ahead-of-time compilation to optimize the full execution path end-to-end Learn more: https://t.co/ABzlzreEJz
- Aug 13, 20264.5x their median
AMD has been upstreaming optimizations for improved FP8 training support in PyTorch/TorchTitan and PyTorch/TorchAO, making FP8 training work out of the box on AMD Instinct GPUs! We trace that effort across every layer of the stack: - Systematically fusing Triton quantization kernels to close the performance gap, collapsing multi-kernel chains into single launches - Accelerating major kernel (grouped GEMM, attention, etc..) performance in PyTorch stack for FP8 training Read the blog: https://t.co/NysAnGQpTp @AMD
- Aug 6, 20263.6x their median
Every gain in pre-training efficiency means researchers can train larger models, run more experiments, and reach frontier capability faster on the same infrastructure. TorchTitan, PyTorch’s native training stack, leverages @NVIDIA contributions to continuously improve its performance on GB300 NVL72. These optimizations compound to deliver approximately 6x higher delivered performance on the same infrastructure, setting a record for pre-training DeepSeek-V3 671B at 1,648 TFLOPs per GPU. Read the full post: https://t.co/jYIP4tgrCE
- Aug 25, 20262.9x their median
And we have a winner. Congrats to @gatere_mark for creating the winning design in our 2026 PyTorch Foundation Flare Pin Community Design Contest! You'll be able to collect this flare pin at the Flare Party on Tuesday, October 20th during PyTorch Conference North America. Learn more & get your ticket to attend #PyTorchCon https://t.co/PW8IN4KiPU #PyTorchPin
- Aug 22, 20262.8x their median
"The PyTorch Conference is really unique in bringing together users, practitioners and developers across the AI spectrum in a way that really no other event does." Niles Burbank @AMD (PyTorch Conference Europe 2026) The open-source AI community is gathering in San Jose! Join us October 20–21 for PyTorch Conference North America 2026 for two days packed with technical sessions, hands-on workshops, and deep dives covering every layer of the AI stack. Register by September 4 to save $200 and secure your pass: https://t.co/m2JLV5jVNb #PyTorchCon
- Aug 20, 20262.6x their median
Leverage a PyTorch-native fine-tuning library with Day-0 Hugging Face checkpoint support for the recently released Alibaba open weights for Qwen3.8-2.4T-A95B (Qwen3.8-Max), its largest open-weight model. Developers can post-train the model for domain-specific use cases with configurable reasoning using PyTorch-native tools and NVIDIA NeMo AutoModel. Train directly on existing checkpoints without model conversion, with support for full SFT or memory-efficient LoRA fine-tuning. Read the full post: https://t.co/GFLEUcULkd
- Aug 13, 20262.4x their median
The PyTorch Certified Associate (PTCA) program offers early-stage practitioners a direct route to build credibility and demonstrate hands-on expertise in AI engineering. As deep learning adoption expands across industries, real-world skills in model design, training, and deployment are essential. PTCA provides a clear pathway to verify your foundational capabilities and stand out in the field. Learn more about the certification and how to enroll here 👉 https://t.co/BSnITGBEhi
- Aug 27, 20262.3x their median
We’re proud to announce the inaugural #PyTorchDayJapan, hosted by PyTorch Foundation, @huggingface, @IBM, and Mitsubishi Electric (@ME_JP_official), on December 10 in Tokyo. #PyTorchDayJapan will bring together PyTorch enthusiasts, machine learning engineers, AI researchers, and industry professionals for technical talks and discussions spanning training, inference, responsible AI, and more. PyTorch Day Japan is dedicated to open source AI and the impact of PyTorch Foundation projects including PyTorch, @vllm_project, @DeepSpeedAI, Ray, Helion, and Safetensors. 🔗 Learn more: https://t.co/w32fnU1tuu
- Aug 21, 20262.3x their median
PyTorch Conference North America 2026 lets contributors hear directly from users about the technical problems they're encountering and connect that feedback to ongoing work in PyTorch. In this clip from #PyTorchCon NA 2025, Tristan Rice @rice_fry (@Meta) describes how conversations with users helped him see that work in areas like fault tolerance and communication was paying off. #PyTorchCon North America returns October 20–21, 2026, in San Jose. Register by September 4 to save $200 and secure your pass: https://t.co/yIZoD2Avei
- Aug 23, 20262.3x their median
The keynote lineup for PyTorch Conference North America is out, offering a clear preview of where our open source ecosystem is heading. The program highlights four main directions: strengthening the core, expanding multi-hardware support, accelerating efficient training and inference, and building next-generation intelligent systems. Register now to see visionary keynotes from @Meta, @RedHat, @Google, @cohere, @AgenticAIFdn, @adaption_ai, @nvidia, @Qualcomm, @inferact, @coreauto, @CrusoeAI and @AWS. Learn more about our keynote speakers here: https://t.co/hPMKCE8DDP Register here: https://t.co/1z0jDhdUZm
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.
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Reading these numbers
A typical post picks up 32 interactions against 509K followers, an engagement rate of 0.006%. Measured over 46 original posts, its engagement rate beats 25% of 3,774 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 0.206% of those impressions turn into an interaction. That is about 3.10% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.8 posts a day over the last 30 days, with activity on roughly 60% of days. Most posts go out around 15:00 UTC, and Thursday is the busiest day of the week. Of the 46 posts sampled, 93% carry an image or video and 96% link out. The account's strongest tracked post pulled 198 interactions, about 6.2x its own typical post. Recurring topics include #pytorchcon, #ai, #cloudnativecon.
- What is PyTorch's engagement rate on X?
- PyTorch (@PyTorch) has an engagement rate of 0.006%, based on the median interactions across 46 original posts from the last 30 days against 508,564 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.006%, PyTorch sits above the 10th percentile of the 36,521 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 @PyTorch have real engagement?
- Its engagement rate beats 25% of the tracked X accounts closest to it in follower count (3,774 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 @PyTorch post?
- Most posts go out around 15:00 UTC, and Thursday is its busiest day, at roughly 1.77 posts per day across the measured window.