Channel
@pytorchblog
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
259subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001173529157 |
|---|---|
| Type | Channel |
| Username | @pytorchblog |
| Created | Between 1 March 2018 and 30 June 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 8 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/pytorchblog |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Aug 2026, 09:16 | 259 | no change |
| 7 Aug 2026, 12:43 | 259 | first reading |
19 posts held, back to 3 December 2023 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 19 posts for this entry, the most recent from 22 May 2024. An engagement rate over an empty window would be a number about nothing.
Lifetime counters from Telegram’s own channel header, read 8 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
May 15, 2024 Achieving Sustainability Goals with PyTorch and Intel AI This post was contributed by Intel AI in partnership with the PyTorch Foundation. https://pytorch.org//blog/achieving-sustainability-goals/ @pytorch
May 02, 2024 A Hitchhiker’s Guide to Speculative Decoding Speculative decoding is an optimization technique for inference that makes educated guesses about future tokens while generating the current token, all within a single forward pass. It incorporates a verification mechanism to ensure the correctness of these speculated tokens, thereby guaranteeing that the overall output of speculative decoding is identical …
May 02, 2024 Announcing PyTorch Docathon June, 2024 We are thrilled to announce the upcoming PyTorch Docathon in June! The Docathon, akin to a hackathon, is an event dedicated to enhancing the quality of the PyTorch documentation with the invaluable assistance of our community. Documentation is a vital component of any technology. By refining it, we can simplify the process for new users to get started with PyTorch…
April 24, 2024 PyTorch 2.3 Release Blog We are excited to announce the release of PyTorch® 2.3 (release note)! PyTorch 2.3 offers support for user-defined Triton kernels in torch.compile, allowing for users to migrate their own Triton kernels from eager without experiencing performance regressions or graph breaks. Tensor Parallelism improves the experience for training Large Language Models using native PyTorch fun…
⚡️ Команда PyTorch разрабатывает библиотеку для обучения LLM под названием torch titan. Сегодня библиотека стала общедоступной на GitHub, но она все еще находится в предрелизном состоянии и активно разрабатывается. - Ссылка на библиотеку: https://github.com/pytorch/torchtitan - Туториал по работе с torch titan: https://www.youtube.com/watch?v=ee5DOEqD35I Библиотека создана для предварительного обучения моделей,…
April 16, 2024 torchtune: Easily fine-tune LLMs using PyTorch We’re pleased to announce the alpha release of torchtune, a PyTorch-native library for easily fine-tuning large language models. https://pytorch.org//blog/torchtune-fine-tune-llms/ @pytorch
April 04, 2024 Accelerating MoE model inference with Locality-Aware Kernel Design 1.0 Summary https://pytorch.org//blog/accelerating-moe-model/ @pytorch
March 13, 2024 Maximizing training throughput using PyTorch FSDP In this blog, we demonstrate the scalability of FSDP with a pre-training exemplar, a 7B model trained for 2T tokens, and share various techniques we used to achieve a rapid training speed of 3,700 tokens/sec/GPU, or 40B tokens/day on 128 A100 GPUs. This translates to a model FLOPS utilization (MFU) and hardware FLOPS utilization (HFU) of 57%. Addition…
February 06, 2024 PyTorch 2 paper and tutorial @ ASPLOS 2024 The PyTorch team is excited to share that our paper on PyTorch 2 has been accepted for presentation at the ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), scheduled to take place from April 27 to May 1, 2024, in San Diego, CA, USA. https://pytorch.org//blog/pytorch-2-paper-tutorial/ @pyt…
February 01, 2024 What's New in PyTorch Documentation Greetings to the PyTorch community! Here is a quick update on PyTorch docs. https://pytorch.org//blog/new-in-docs/ @pytorch
January 16, 2024 Accelerating Triton Dequantization Kernels for GPTQ TL;DR https://pytorch.org//blog/accelerating-triton/ @pytorch
January 09, 2024 Accelerate AI models on GPU using Amazon SageMaker multi-model endpoints with TorchServe, saving up to 75% on inference costs Multi-model endpoints (MMEs) are a powerful feature of Amazon SageMaker designed to simplify the deployment and operation of machine learning (ML) models. With MMEs, you can host multiple models on a single serving container and host all the models behind a single endpoint. …
Showing the 12 most recent of 19 posts we hold for @pytorchblog. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 8 August 2026 — this entry's latest reading, not the date you are reading this.
“Pytorch” (@pytorchblog), 259 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/pytorchblog.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.