An open standard for AI inference backed by Google Cloud, IBM, Red Hat, Nvidia and more was given to the Linux Foundation for stewardship in further proof training has been superseded by inference in ...

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Amazon Web Services plans to deploy processors designed by Cerebras inside its data centers, the latest vote of confidence in the startup, which specializes in chips that power artificial-intelligence ...

No GPU fleet runs at full capacity around the clock. InferenceSense™ automatically fills idle cycles with paid AI inference workloads—and shares the revenue with you. FriendliAI, The Frontier AI ...

Qwen3-Coder-Next 80B 36 DeltaNet + 12 Attention, 512 experts top-10 + shared 80B total / 3B active ~2.1 tok/s (Q4 matmul) Qwen3-30B-A3B 48 Attention, 128 experts top-8 30B total / 3B active ~55 tok/s ...

Adding big blocks of SRAM to collections of AI tensor engines, or better still, a waferscale collection of such engines, turbocharges AI inference, as has been shown time and again by AI upstarts ...

Much of the conversation around AI today is focused on building cloud capacity and massive data centers to run models. Companies like Apple and Qualcomm are in the early stages of making on-device AI ...

Cloudflare has released the Agents SDK v0.5.0 to address the limitations of stateless serverless functions in AI development. In standard serverless architectures, every LLM call requires rebuilding ...

In the world of Large Language Models (LLMs), speed is the only feature that matters once accuracy is solved. For a human, waiting 1 second for a search result is fine. For an AI agent performing 10 ...

Lowering the cost of inference is typically a combination of hardware and software. A new analysis released Thursday by Nvidia details how four leading inference providers are reporting 4x to 10x ...

Agentic applications are LLM that iteratively invoke external tools to accomplish complex tasks. Such tool-based agents are rapidly becoming the dominant paradigm for deploying language models in ...