For the last 18 months, the CISO playbook for generative AI has been relatively simple: Control the browser.Security teams tightened cloud access security broker (CASB) policies, blocked or monitored traffic to well-known AI endpoints, and routed usage through sanctioned gateways. The operating model was clear: If sensitive data leaves the network for an external API call, we can observe it, log it, and stop it. But that model is starting to break.A quiet hardware shift is pushing large language model (LLM) usage off the network and onto the endpoint. Call it Shadow AI 2.0, or the “bring your own model” (BYOM) era: Employees running capable models locally on laptops, offline, with no API calls and no obvious network signature. The governance conversation is still framed as “data exfi [...]
For the last 24 months, one narrative justified every over-provisioned data center and bloated IT budget: the GPU scramble. Silicon was the new oil, and H100s traded like contraband. Reserve capacity [...]
Baseten, the AI infrastructure company recently valued at $2.15 billion, is making its most significant product pivot yet: a full-scale push into model training that could reshape how enterprises wean [...]
Every GPU cluster has dead time. Training jobs finish, workloads shift and hardware sits dark while power and cooling costs keep running. For neocloud operators, those empty cycles are lost margin.The [...]
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 redu [...]
Enterprises expanding AI deployments are hitting an invisible performance wall. The culprit? Static speculators that can't keep up with shifting workloads.Speculators are smaller AI models that w [...]
Nvidia’s $20 billion strategic licensing deal with Groq represents one of the first clear moves in a four-front fight over the future AI stack. 2026 is when that fight becomes obvious to enterprise [...]
The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use inference-tim [...]
Enterprises can't fix their GPU waste problem because the fix makes the problem worse. Releasing idle capacity would improve utilization, but the same shortage driving GPU prices up is exactly wh [...]
Presented by DigitalOceanFrom refactoring codebases to debugging production code, AI agents are already proving their value. But scaling them in production remains the exception, not the rule. In Digi [...]