Disaggregated Inference Is Live! At COMPUTEX, we demonstrated the world's first disaggregated inference cloud for AI agents.
GPUs for prefill. RDUs for decode. CPUs for orchestration. The result: faster agent workflows and better economics than homogeneous infrastructure.
COMPUTING
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SambaNova demonstrates first disaggregated inference cloud for AI agents
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Revolut’s transaction foundation model accelerated by NVIDIA and Nebius
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Revolut built a transaction foundation model – accelerated by the NVIDIA full-stack platform on Nebius – to improve fraud detection, product recommendations, and other use cases in financial services.
— NVIDIA (@nvidia) 25 juin 2026
The results:
📈 2.3x better credit risk accuracy
⚡ Up to 5x higher training… pic.twitter.com/vAtbVJXfPZRevolut built a transaction foundation model – accelerated by the NVIDIA full-stack platform on Nebius – to improve fraud detection, product recommendations, and other use cases in financial services. The results: 2.3x better credit risk accuracy Up to 5x higher training
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Building Gradio server app for Ornith-1.0-9B with glm 5.2
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glm 5.2 in hf-claude building a gradio server app for Ornith-1.0-9B
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Help Eyal critique running LLM inference in browser
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who wants to help eyal poke holes in this approach to run LLM inference… in browser?
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Latency as the new AI battleground and edge AI importance
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I wrote more about this in my latest article: “Milliseconds Are the New AI Battleground.” It explores how latency can affect operational efficiency, why edge AI matters in low-latency environments, and how organizations are rethinking AI infrastructure closer to where
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Evaluating AI insight speed for manufacturing, logistics, and edge
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Many organizations are now evaluating how quickly AI insights can influence operations. Because in environments like manufacturing, logistics, and industrial automation, even small delays can affect efficiency, precision, and responsiveness. Edge environments can help reduce
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Edge AI reduces latency for responsive operations near data
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The goal is not to replace the cloud.
It’s to support decision-making closer to where data is generated. That’s where edge AI environments can help reduce latency and support more responsive operations. This is exactly what Edge Control from @TMobileBusiness is designed to -
DFlash: Drop-in Speculative Decoding for SGLang, vLLM, TensorRT-LLM
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/7 Drop-in for SGLang, vLLM, and TensorRT-LLM. No code refactoring. SGLang:
–speculative-algorithm DFLASH
–speculative-draft-model-path z-lab/Qwen3-8B-DFlash-b16 vLLM: via the Speculators library (
http://
docs.vllm.ai/projects/specu
lators
…, algorithm "dflash") MIT license. ICML 2026 accepted. -
Hyperagent gives each agent its own dedicated cloud machine
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We spent two years calling things agents that fall over the second nobody's watching.
— Chubby♨️ (@kimmonismus) 25 juin 2026
A setup tied to one laptop, one wifi, and one person awake at 1am to restart it when it breaks is closer to a pager than to autonomy.
Hyperagent gives every agent its own cloud machine that… https://t.co/VAkGZuAUDoWe spent two years calling things agents that fall over the second nobody's watching. A setup tied to one laptop, one wifi, and one person awake at 1am to restart it when it breaks is closer to a pager than to autonomy. Hyperagent gives every agent its own cloud machine that
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Deep Dive into LSTM and xLSTM
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Deep Dive into LSTM and xLSTM! #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Deep-Dive-into
-LSTM
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