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 ...

Inference will take over for training as the primary AI compute moving forward. Broadcom has struck gold with its custom ASICs for AI hyperscalers. Arm Holdings should benefit immensely as inference ...

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 ...

Abstract: We present a generative modeling approach based on the variational inference framework for likelihood-free simulation-based inference. The method leverages latent variables within ...

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 ...

The creators of the open source project vLLM have announced that they transitioned the popular tool into a VC-backed startup, Inferact, raising $150 million in seed funding at an $800 million ...

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 ...

“Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundamentally different from training. Exacerbated by recent AI ...

In recent years, the big money has flowed toward LLMs and training; but this year, the emphasis is shifting toward AI inference. LAS VEGAS — Not so long ago — last year, let’s say — tech industry ...

Abstract: We propose a control barrier function (CBF) formulation for enforcing equality and inequality constraints in variational inference. The key idea is to define a barrier functional on the ...