ai-hardware-selectionlisted

# AI Hardware Selection ## The metric that usually decides: memory, then bandwidth For LLM **inference**, the binding constraint is typically **VRAM/HBM capacity** (weights + KV-cache must fit) and **memory bandwidth** (decode is memory-bound) — not raw FLOPS. Size first: `weights ≈ params × bytes/param` (e.g. 70B × 2B(FP16) ≈ 140GB → multi-GPU or quantize). Add KV-cache (grows with context × batch). Only then look at TOPS. ## Accelerator families | Type | Strength | Use | |---|---|---| | **GPU** (NVIDIA H/B-series, AMD MI) | Flexible, huge ecosystem, HBM | Training + inference, the default | | **TPU** | Matmul-dense, pod-scale interconnect | Large-scale training/inference on GCP | | **NPU** | Perf/Watt at low power | Edge / mobile / AI-PC inference | | **FPGA** | Custom low-latency dataflow | Niche ultra-low-latency / fixed pipelines | | **CPU** | Available, fine for small/batch | Small models, embeddings, light load | ## Other levers - **Interconnect** (NVLink, InfiniBand): decisive for multi-GPU training and tensor parallelism — bandwidth between accelerators bounds scaling. - **Precision support**: FP8/INT4 support multiplies effective throughput/capacity. - **Cost/Watt & TCO**: cloud per-hour vs owned; power/cooling; utilization. The honest metric is **cost per token (or per request) at target latency**. - **Training vs inference**: training needs FLOPS + interconnect + memory; inference needs memory capacity/bandwidth + latency. ## When to recommend what - Def