MarktechPost·
We implement xFormers, a practical toolkit for fast, memory-efficient Transformer models on GPUs. We validate memory-efficient attention against a standard implementation, then compare speed and memory across sequence lengths. We work through causal masking, packed variable-length sequences, grouped-query attention, and custom ALiBi biases. Finally, we combine these into a trainable GPT-style model with SwiGLU layers and automatic mixed-precision training. The post How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention appeared first on MarkTechPost.
Read full articleAMD's AI and GPU advancements could significantly boost investor confidence, potentially driving long-term growth and higher stock valuations. The post Wolfe Research reiterates AMD price target at $450 on AI and GPU growth appeared first on Crypto Briefing.
$IREN secured 96% of the $5.81bn GPU capex for its Microsoft contract at a low single-digit all-in financing cost. This was enabled by by the Microsoft lease itself and carries investment-grade credit rating. The following guest post comes from BitcoinMiningStock.io, a public markets intelligence platform delivering data on companies exposed to bitcoin mining, artificial intelligence, […]
A systems-level deep dive into the hidden microarchitectural costs of Kubernetes GPU time-slicing, and what it actually costs to co-locate Agentic AI workloads. The post GPU Time-Slicing for Concurrent LLM Agents on Kubernetes appeared first on Towards Data Science.
AMP PBC's GPU utility model could democratize AI compute access, leveling the playing field for smaller AI teams against tech giants. The post AMP PBC wants to turn GPUs into a utility, and it has $1.3 billion to try appeared first on Crypto Briefing.
HIVE's AI pivot could redefine its market position, but execution risks and shifting GPU demand may challenge its ambitious growth targets. The post HIVE Digital Technologies targets 500 MW capacity by 2028 as AI pivot accelerates appeared first on Crypto Briefing.
Learn how to write an effective essay hook that captures attention, introduces your topic, and creates a strong first impression. Explore popular hook types, examples, common mistakes, and practical tips for engaging readers from the very first sentence.
We’re seeing an interesting infrastructure tug of war today where GPU clouds are being pulled in two directions. For the economics of AI to work, the enterprise market needs to carve expensive hardware into smaller, shareable units and hand it to customers on demand, similar to how CPUs are doled in public cloud infrastructure. But the more the providers push GPUs to behave like elastic cloud infrastructure, the more they run into the reality that this GPU hardware was never built for safe multitenant use, fast fault recovery, or clean isolation between workloads. That tension is becoming one of the defining operational problems of the AI infrastructure market. When a gamer launches Steam or the Epic Games Store on their laptop, they don’t have to worry about which GPU is being scheduled, how memory is going to be divided, or really any of the security boundaries or hardware assignment issues on their PC. For consumer PCs, these issues are not just hidden from view, they are irrelevant
In this tutorial, we implement a hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for CUDA-style kernels in Python. We prepare a Colab-friendly environment and check GPU, driver, CUDA, and cuTile availability before running kernels. We then build tiled vector addition, matrix addition, and matrix multiplication, keeping a PyTorch fallback so the notebook stays executable. We validate correctness against PyTorch and benchmark median runtimes at every stage. The post NVIDIA cuTile Python Tutorial: Building Tiled GPU Kernels for Vector Addition, Matrix Addition, and Matrix Multiplication in Colab appeared first on MarkTechPost.
