#TPU hashtag - Bluesky

@spmrider.berlin.social.ap.brid.gy

1 day ago

Bisher habe ich mich beim #3DDruck auf #PETG (und selten #PLA) beschränkt, ab und zu mal #TPU. Heute dann Premiere mit #ASA - ich bin gespannt.
Zunächst mal dauert es erheblich länger, bis der Druck startet, um eine hohe und gleichmäßige Bauraumtemperatur zu erreichen. Das erste kleine […]

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Resist All War 🕊️ | 🌔

@resistallwar.bsky.social

2 days ago

TPU Tubes: How fast are they? TPU tubes have been widely hailed as the 'next big thing' in the mainstream media. TPU tubes are lighter, faster and more durable than butyl tubes, while holding air far better than latex tubes. That...

TPU Tubes: How fast are they?
#cycling #TPU #biketires #ReneHerse #rollingresistance #bicycle

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AI & ML News

@ai-news.at.thenote.app

3 days ago

DRA: A new era of Kubernetes device management with Dynamic Resource Allocation Large language models' growth necessitates efficient GPU and TPU utilization, making resource management critical. Kubernetes is becoming the standard platform for running these AI models in the enterprise. NVIDIA and Google have donated Dynamic Resource Allocation (DRA) drivers to the Kubernetes community, promoting community growth and innovation. DRA, now stable in Kubernetes, provides a flexible, request-based model for hardware management. Unlike traditional Device Plugins, DRA eliminates manual node pinning and offers flexible parameterization of hardware requests. DRA introduces DeviceClasses, enabling workload needs to be decoupled from specific hardware details. ResourceSlices describe available hardware capabilities, while ResourceClaims define application requirements in detail. The scheduler uses these to match workloads with optimal hardware, improving resource utilization. DRA in GKE simplifies hardware management, making it more intelligent and accessible for dynamic environments. The Kubernetes AI Conformance program has deemed DRA support as a core requirement for AI/ML workloads. Users can find resources to learn more and get started with DRA.

DRA: A new era of Kubernetes device management with Dynamic Resource Allocation

Large language models' growth necessitates efficient GPU and TPU utilization, making resource management critical. Kubernetes is becoming the standard platform for running these AI…

Telegram AI Digest
#gpu #nvidia #tpu

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AI & ML News

@ai-news.at.thenote.app

5 days ago

Kubernetes as AI Infrastructure: Google Cloud, llm-d, and the CNCF Google Cloud is focused on providing infrastructure for large language model (LLM) builders and AI-native companies. They are introducing llm-d, a Cloud Native Computing Foundation (CNCF) Sandbox project, with founding contributions from Google Cloud, Red Hat, IBM, CoreWeave, and NVIDIA. This open-source initiative aims for an "any model, any accelerator, any cloud" vision to foster open standards. Google Cloud's contribution reflects their leadership in open-source innovation, ensuring distributed AI inference uses open standards. They are enhancing Kubernetes for LLM inference, launching GKE Inference Gateway that utilizes llm-d Endpoint Picker for intelligent request routing. This routing significantly improves performance, shown by reduced latency and increased cache hit rates using Vertex AI for projects like Qwen Coder and DeepSeek. Kubernetes LeaderWorkerSet (LWS) API, developed by Google, helps orchestrate multi-node AI deployments, including TPUs and GPUs. Google is also improving vLLM for Cloud TPUs, offering significant throughput gains which aid optimization on both TPUs and NVIDIA GPUs. Google is collaborating with the open-source community, aiming to build next-generation AI infrastructure, advocating open and accessible AI. The text encourages others to explore llm-d, learn more & contribute to shaping the future of AI inference.

Kubernetes as AI Infrastructure: Google Cloud, llm-d, and the CNCF

Google Cloud is focused on providing infrastructure for large language model (LLM) builders and AI-native companies. They are introducing llm-d, a Cloud Native Computing Foundation (CNCF) San…

Telegram AI Digest
#deepseek #gpu #tpu

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AI и ML Новости

@ai-ru.at.thenote.app

5 days ago

A developer’s guide to training with Ironwood TPUs

Руководство разработчика по обучению с помощью TPU Ironwood

Текст обсуждает достижения TPU Ironwood от Google, разработанного для обучения больших моделей ИИ. Ironwood использует специально разработанную инфраструктуру, включая передовые межсоединения и память …

Telegram ИИ Дайджест
#ai #news #tpu

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AI & ML News

@ai-news.at.thenote.app

5 days ago

A developer’s guide to training with Ironwood TPUs The text discusses the advancements of Google's Ironwood TPU, designed for training large AI models. Ironwood utilizes a custom-designed infrastructure, including advanced interconnects and high-bandwidth memory, to handle the demanding computational needs. Key to Ironwood's performance is its integrated hardware and software co-design, leveraging features like Compiler-Centric XLA, and optimized kernels. The overview explores strategies within the JAX and MaxText ecosystems to maximize efficiency on Ironwood. FP8 precision support in Ironwood allows for doubled throughput using the Qwix library. Tokamax kernels, including Splash Attention and Megablox GMM, are designed to mitigate bottlenecks in memory access. Offloading collectives to SparseCore ensures TensorCores remain dedicated to model computations. Fine-tuning VMEM improves overall execution speed, and optimal sharding strategies are crucial for various model types. FSDP, TP, EP, and CP are the supported parallelism techniques. These optimization methods, combined with Ironwood's architectural strengths, create a highly performant platform. The close collaboration between hardware, compilers, and frameworks allows for extracting maximum performance from AI infrastructure. The provided resources offer further details on each optimization method, and the text highlights the system-level performance advantages of Ironwood.

A developer’s guide to training with Ironwood TPUs

The text discusses the advancements of Google's Ironwood TPU, designed for training large AI models. Ironwood utilizes a custom-designed infrastructure, including advanced interconnects and high-bandwidth memory, …

Telegram AI Digest
#ai #jax #tpu

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TechRefreshing

@anupyadav.bsky.social

1 week ago

What Is A TPU? A Beginner's Guide To Google's AI Accelerator (2026) Discover what a TPU is, how Google's Tensor Processing Unit works, and why it's powering the future of AI — from Ironwood specs to real-world use cases.

What Is a TPU? A Beginner's Guide to Google's AI Accelerator (2026 Edition)
techrefreshing.com/what-is-a-tp...
#TPU #AI #GoogleCloud #MachineLearning #Ironwood #AIChips #DeepLearning

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