PyTorch (@PyTorch) / X

PyTorch

3,440 posts

PyTorch

@PyTorch

Tensors and neural networks in Python with strong hardware acceleration. PyTorch is an open source project at the Linux Foundation. #PyTorchFoundation

pytorch.org

Joined September 2016

PyTorch
@PyTorch
9h
PyTorch 2.13 brings FlexAttention to Apple Silicon, cuts peak memory by up to 4× for large-vocabulary models with nn.LinearCrossEntropyLoss, and updates distributed training, compilation, profiling, and on-device inference. Our live Q&A examined CUDA version support, CuTeDSL in
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PyTorch
@PyTorch
10h
Congratulations to SMG on graduating from @lightseekorg and moving to an independent home. As part of the PyTorch ecosystem, SMG will continue working with maintainers from @vllm_project and TokenSpeed on shared frontend and gateway infrastructure for LLM inference.
LightSeek Foundation
@lightseekorg
13h
Today SMG (Shepherd Model Gateway), an engine-agnostic gateway for LLM inference, has officially graduated from LightSeek Foundation and moves to a new independent home: github.com/smg-project/smg SMG was the first project incubated by the LightSeek Foundation and was recently
PyTorch
@PyTorch
15h
⏰ Today is your final chance to save $400 on #PyTorchCon North America, Oct 20-21 in San Jose! Early Bird pricing ends tonight at 11:59 PM PDT. 📅 See what's on the schedule: bit.ly/3Ra1oUx 🎟️ Register now: bit.ly/4sh3DSw
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PyTorch
@PyTorch
16h
Great to see! Later this year, RadixArk will roll out SGL-torchtpu as an additional @radixark PyTorch-native TPU backend, featuring eager execution, @PyTorch ecosystem compatibility, and MPMD support. It will let any AI researcher or engineer in the industry run LLMs with
RadixArk
@radixark
Jul 30
RadixArk and Google Cloud are joining forces with the SGLang community to make TPU a drop-in, cost-efficient path to frontier inference. SGL-JAX already serves the major open model families on the latest TPU generations: Gemma, Qwen, DeepSeek, GLM, Kimi, Ling, MiniMax, MiMo,
PyTorch
@PyTorch
Jul 30
FBTriton is the Triton repo where Meta develops its experimental GPU optimization solutions (including TLX/torchTLX and autoWS). It has served as an optimization runway for Meta engineers and external partners, including NVIDIA, AMD, and academic collaborators, to co-design
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