Latency-Sensitive Inference Design Principles Latency-sensitive inference is where model performance stops being a research score and becomes a service contract. A user does not experience average tokens per second. They experience how long it takes for a response to begin, how smoothly it streams, and whether it stalls at the worst possible moment. Most of […]

Virtualization and Containers for AI Workloads AI workloads are unusually sensitive to environment details. A small mismatch in driver versions, runtime libraries, or kernel settings can turn a working system into an intermittent failure. At the same time, AI infrastructure is increasingly shared: multiple teams, multiple models, mixed priorities, and heterogeneous hardware. Virtualization and containers […]

Training vs Inference Hardware Requirements Training and inference both run neural networks, but they stress hardware in different ways and reward different design choices. Training is a throughput game with large working sets, heavy communication, and long-running jobs. Inference is a service game, where latency, cost per output, and reliability under variable load matter as […]

Supply Chain Considerations and Procurement Cycles AI infrastructure is not only a technical problem. It is also a supply problem. When a workload becomes GPU-bound, the constraint is rarely a clever piece of code. The constraint is often whether you can acquire, deploy, and keep enough reliable compute online at the right cost. Supply chain […]

Storage Pipelines for Large Datasets A modern AI stack can burn through GPU time at a rate that makes storage look slow, even when storage is “fast” by traditional standards. This is why storage pipelines matter. If data cannot reach the GPUs in the right shape and at the right rate, the cluster becomes a […]

Serving Hardware Sizing and Capacity Planning Modern AI systems rarely fail because a model is unavailable. They fail because capacity is misread: tokens are cheaper than expected until a spike arrives, latency looks fine until the tail collapses, an innocuous feature doubles average context length, or a queue forms and never drains. Serving is not […]

RDMA and GPUDirect: Zero-Copy Data Paths and Tail Latency When AI systems scale, moving bytes becomes the hidden tax that controls cost and latency. The system can have powerful accelerators and still feel slow because data takes too many hops, too many copies, and too many kernel transitions. RDMA and GPUDirect are families of techniques […]

Quantization Formats and Hardware Support Quantization is the set of techniques that shrink the numeric representation of a model so it runs faster, cheaper, or in smaller memory footprints than a full‑precision baseline. In practice, quantization is not a single switch. It is a design space with consequences that reach from kernel choice to capacity […]

Power, Cooling, and Datacenter Constraints AI infrastructure often looks like a software story from a distance: models, prompts, tools, orchestration. Up close, the pace and price of deployment are frequently set by physical constraints. Power delivery, cooling capacity, rack density, and facility readiness decide how many accelerators you can actually run, how reliably they operate, […]

On-Prem vs Cloud vs Hybrid Compute Planning Compute planning for AI systems is a strategy problem disguised as a hardware problem. The decision is not only where inference or training runs today, but how quickly the system can scale, how resilient it is to failures, how predictable the cost curve becomes, and how much operational […]

NUMA and PCIe Topology: Device Placement for GPU Workloads AI workloads move huge volumes of data through a machine that was not built as a single, uniform pool of resources. Modern servers are mosaics: multiple CPU sockets, multiple memory controllers, multiple PCIe root complexes, and often multiple layers of switches between devices. Two GPUs in […]

Multi-Tenancy Isolation and Resource Fairness Multi-tenancy is what turns AI compute from a lab asset into shared infrastructure. It is the difference between a single team owning a dedicated cluster and many teams, customers, or workloads sharing the same fleet. Done well, multi-tenancy lowers unit cost, increases utilization, and makes capacity more flexible. Done poorly, […]