AI & ML

Introduction Enterprise GPU design becomes confused when several different decisions are compressed into one question: “How should we share the GPU?” That question mixes hardware assignment, virtualization, Kubernetes scheduling, tenant isolation, business priority, and service-level commitments. The result is often a platform that advertises many GPU “slices” but cannot explain what each slice guarantees. A […]

Students helped design the A.I.-powered creation as a young female with dark hair and an upbeat personality. Then came the outrage.

Anthropic and OpenAI are clashing with the rest of the tech industry over whether “open-source” models from China should be freely available or restricted.

As start-ups like Anthropic and OpenAI prepare to go public, nonprofits are hoping for a secondary giving windfall from the new tech ultrawealthy.

Our animosity toward data centers is really more about our anxieties about A.I.

The architecture distinction is visible before the protocol details begin. A2A operates horizontally between independently operated agents. MCP operates vertically between an AI application or agent and the tools, data, prompts, and enterprise capabilities it consumes. That difference is the foundation for the entire protocol decision. Enterprise AI teams are increasingly asking whether they should

TL;DR An expected workload count is not a GPU requirement. Thirty concurrent notebooks, RAG services, inference endpoints, fine-tuning jobs, or distributed training runs can create radically different demands for GPU memory, accelerator time, CPU, system memory, storage throughput, metadata operations, network bandwidth, scheduling policy, and failure reserve. Capacity planning should begin by defining workload service

TL;DR The NVIDIA RAG Blueprint is not one application pod. It is a coordinated retrieval platform that combines an ingestion service, a RAG server, NVIDIA NIM microservices, NV-Ingest, a vector database, object storage, model caches, and supporting Kubernetes operators. For the current 2.6.0 release, Elasticsearch is the default vector database and SeaweedFS is the default

TL;DR A multivendor private AI platform is not operationally complete when the hardware is installed, the GPUs are visible, and the first model endpoint responds. It is complete when the organization knows who performs the first diagnostic action when any part of the stack fails. The customer should retain one accountable service owner and one

The industry is courting theologians and philosophers. But it is asking them the wrong questions