The Cold Start Problem: Why Serverless GPU Inference Is Hard, and Who Is Solving It
Pay-per-second GPU inference is the dream; loading a 40 GB model into VRAM on demand is the nightmare. How serverless GPU providers attack cold starts, and the workloads where serverless now genuinely wins.
InfiniBand vs Ethernet: The Network War Underneath Your GPU Cluster
Multi-GPU training lives or dies on the fabric between chips, and 96% of organizations hit network-related AI problems last year. InfiniBand vs modern Ethernet: performance, cost, lock-in, and when each wins.
Noisy Neighbors in GPU Clouds: Why Identical GPUs Deliver Different Performance
Rent the same H100 from three providers and you can get three very different results. Multi-tenancy, network fabric, and storage adjacency explain the variance. How to benchmark before you commit.
Small Models, Big Savings: When a 7B Model Beats a Frontier LLM on Cost
Frontier models are remarkable and expensive. For a large share of production tasks, a small model matches the quality at a tenth of the cost. The right-sizing framework, with infrastructure math.
The High-Performance Storage War: Parallel File Systems vs Object Storage for AI Training
AI training created a storage battleground: parallel file systems promise GPU-saturating throughput while object storage promises simplicity and scale. The honest comparison, and the hybrid most teams land on.
Kubernetes Was Not Built for GPUs: What Breaks and What Comes Next
Kubernetes was designed for stateless, fungible compute. GPU training is stateful, topology-sensitive, and interruption-hostile. Where it breaks, the current fixes, and the post-Kubernetes patterns emerging.
Edge AI Infrastructure: When Inference Should Leave the Data Center
Some inference belongs milliseconds from the user; some never should leave the data center. The edge AI decision framework, the hardware landscape, and the storage synchronization problem in the middle.
Model Checkpointing at Scale: The Storage Bill Hiding Inside Every Training Run
Every serious training run writes checkpoints, and at modern model sizes those checkpoints become a petabyte-scale storage problem. The architecture patterns and cost math nobody scopes upfront.
Power and Cooling Are the New GPU Shortage: Inside the Data Center Bottleneck
Industry surveys now find power and cooling constraints limiting AI deployments before GPU availability does. Why megawatts replaced chips as the scarce resource, and how it shapes provider choice.
Vector Database Storage: Choosing Infrastructure for Embeddings at Scale
Every RAG system stands on a vector database, and at scale those embeddings become a serious storage problem. Memory vs NVMe vs object storage trade-offs, real cost math, and architecture patterns that hold up.
