The Archive

Uber exhausts 2026 AI budget by April due to rising Claude Code inference costs.

Community release of Kimi K2.6 GGUF Q4_X quantization with 584GB+ VRAM requirement and imatrix optimization planned.

Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, how AI is playing a part.

John Gruber commentary on Apple's declining app ecosystem advantage as third-party developer motivation wanes.

Slurm is an open source cluster management and job scheduling system for Linux. It manages job scheduling for over 65% of TOP500 systems. Most organizations... Slurm is an open source cluster management and job scheduling system for Linux. It manages job scheduling for over 65% of TOP500 systems. Most organizations running large-scale AI training have years of investment in Slurm job scripts, fair-share policies, and accounting workflows. The challenge is getting Slurm scheduling capabilities onto Kubernetes—the standard platform for managing GPU… Source

In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition... In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition (ASR) or text-to-speech (TTS) models may require only 10 GB of VRAM, yet occupy an entire GPU in standard Kubernetes deployments. Because the scheduler maps a model to one or more GPUs and can’t easily share across GPUs across models… Source

As large language model (LLM) inference workloads grow in complexity, a single monolithic serving process starts to hit its limits. Prefill and decode stages... As large language model (LLM) inference workloads grow in complexity, a single monolithic serving process starts to hit its limits. Prefill and decode stages have fundamentally different compute profiles, yet traditional deployments force them onto the same hardware, leaving GPUs underutilized and scaling inflexible. Disaggregated serving addresses this by splitting the inference pipeline… Source

Every AI cluster running on Kubernetes requires a full software stack that works together, from low-level driver and kernel settings to high-level operator and... Every AI cluster running on Kubernetes requires a full software stack that works together, from low-level driver and kernel settings to high-level operator and workload configurations. You get one cluster working, and spend days getting the next one to match. Upgrade a component, and something else breaks. Move to a new cloud and start over. AI Cluster Runtime is a new open-source project designed… Source

NVIDIA Run:ai v2.24 introduces time-based fairshare, a new scheduling mode that brings fair-share scheduling with time awareness for over-quota resources to... NVIDIA Run:ai v2.24 introduces time-based fairshare, a new scheduling mode that brings fair-share scheduling with time awareness for over-quota resources to Kubernetes clusters. This capability, built on the open source KAI Scheduler that powers NVIDIA Run:ai, addresses a long-standing challenge in shared GPU infrastructure. Consider two teams with equal priority sharing a cluster. Source

Uber Head of AI discusses on-demand experience optimization using AI integration.

We’ve scaled Kubernetes clusters to 7,500 nodes, producing a scalable infrastructure for large models like GPT-3, CLIP, and DALL·E, but also for rapid small-scale iterative research such as Scaling Laws for Neural Language Models.