
CPU, GPU or TPU? Choosing an Accelerator Beyond Peak FLOPS
Compare AI processors using actual workload requirements, memory bandwidth, supported software and cost per completed task.
ILLUSIONCLOUD BLOG
AI systems, processors, accelerators and the economics of computing.
ARTICLES

Compare AI processors using actual workload requirements, memory bandwidth, supported software and cost per completed task.

Understand rack power budgets, cooling loops, heat rejection and why facility PUE cannot measure AI workload efficiency.

Understand GPU and CPU attestation, key-release policies and the limits of confidentiality on hosted accelerators.

Compare FP8 and FP4 formats, scaling overhead, weight-only quantization and hardware support without conflating bit width with application performance.

Compare PCIe, NVLink, InfiniBand and Ethernet by topology, bandwidth direction and distributed workload communication.

Compare hardware GPU partitions, Kubernetes time-slicing and virtual GPU allocations, including memory, isolation and contention.

Learn to distinguish model memory capacity, peak HBM bandwidth, batching and interconnect limits when choosing AI accelerators.

Calculate transformer KV-cache memory from layer count, KV heads, token length and precision, with examples for concurrent requests.

Interpret MLPerf scenarios, model quality, accelerator count, benchmark divisions and measured system power.

Compare training memory, inference latency, batching and cost per useful result to understand GPU infrastructure economics.

NVIDIA confirms Jensen Huang's resignation after his admission of infidelity. A sourced timeline, verified NVDA closing prices and the leadership risks.

Examine how GPU hardware costs influence cloud gaming capacity and service pricing.