Abstract: Probabilistic graphical models are useful for modelling stochastic phenomena for doing inferences and reasoning under uncertainty. Especially, chain graph models and Bayesian networks can be ...

Stanford adjunct professor and successfully exited founder Zain Asgar just raised an $80 million Series A for a startup that solve the AI inference bottleneck problem in an astute way. The round was ...

A significant shift is under way in artificial intelligence, and it has huge implications for technology companies big and small. For the past half-decade, most of the focus in AI has been on training ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Andrew Harmel-Law and a panel of expert ...

Nvidia is not just a leader in training, but also in AI inference. AMD has carved out a nice niche in inference, and also has a nice agentic AI opportunity with its CPUs. Broadcom is set to benefit ...

As AI workloads shift from centralized training to distributed inference, the network faces new demands around latency requirements, data sovereignty boundaries, model preferences, and power ...

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 ...

Modal Labs, a startup specializing in AI inference infrastructure, is talking to VCs about a new round at a valuation of about $2.5 billion, according to four people with knowledge of the deal. Should ...

With that, the AI industry is entering a “new and potentially much larger phase: AI inference,” explains an article on the Morgan Stanley blog. They characterize this phase by widespread AI model ...

Google researchers have warned that large language model (LLM) inference is hitting a wall amid fundamental problems with memory and networking problems, not compute. In a paper authored by ...