Nvidia expands Nemotron LLM family with hyper-efficient models for agentic systems

SiliconANGLE · 1 week ago · 27 ● 5 sources

Nvidia launched customizable Nemotron 3.5 Super LV and agent-focused Nemotron 3.5 Bolt LLMs for always-on AI agents, with an additional smaller Bolt E4B planned later this month. Super VL is an open 120-billion-parameter model trained on about 26 trillion tokens. Organizations can download the open weights to run and post-train models behind their own firewalls, while Bolt models enable local and edge deployment within 24 GB or as little as 4 GB.

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Agentic AI Large Language Models NVIDIA Open-Weight Models

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Cerebras positions inference speed as a competitive edge in agentic AI

SiliconANGLE · 1 week ago · 18

Cerebras Systems argued that inference speed is a key competitive advantage for agentic AI systems. It says its inference performance is 10 to 15 times faster than competing GPU-based platforms. As enterprises shift toward maximizing value per token, users are reportedly favoring Cerebras’ fastest tokens and infrastructure.

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AI Infrastructure Agentic AI GPU Infrastructure AI Efficiency

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Pure UI in the Agentic Era

mattrothenberg.com · 1 week ago · 19

Pure UI is presented as a state-only rendering approach for agent-written interfaces, where the UI is derived directly from explicit inputs like roles, plans, and feature flags. It specifically calls out feature flags as one of the explicit state inputs to cover combinations before release. Teams can render and inspect every state permutation during development instead of discovering UI issues after deployment.

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Developer Tools AI Agents & Workflows AI Assistants Agentic AI

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The Sequence Opinion - Issue 951: The Whole Stack Wins

TheSequence · 1 week ago · 9

The Sequence Opinion argues that future AI trip-replanning agents will feel like one end-to-end product spanning application, model, live data, security controls, and compute rather than separate components competing for value.

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AI Infrastructure AI Adoption AI Agents & Workflows Agentic AI

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Plan or Retrieve First? A Scale Study of Tool Discovery Architectures

Apple Machine Learning Research · 1 week ago · 16

Tool-augmented LLM agents benchmark two tool-discovery architectures—Retrieve-then-Plan and Plan-then-Retrieve—on ToolBench to see which finds the right APIs before execution. PtR beats RtP on multi-tool queries across retrieval budgets k=1 to 20 and registry sizes from 1k to 47k APIs. The results imply complexity-adaptive routing (potentially using RtP’s top-1 retrieval score) to choose between the two pipelines.

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Agentic AI Large Language Models AI Benchmarking Retrieval-Augmented Generation

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