The 'Fastest' Hardware Myth When we talk about the 'fastest' AI inference hardware, we often confuse two distinct goals: lowest latency (critical for interactive chat) and highest throughput (essential for massive-scale batch processing). A chip that delivers the most tokens per second might still fail your users if the Time-to-First-Token (TTFT) is high or tail latency spikes under load. In 2026, the hardware landscape is diverse. To pick the right tool, you have to look at your workload, your budget, and your specific capacity needs. The Hardware Breakdown Hardware Best For Main Trade-off NVIDIA H200/B200 Interactive/High Throughput Availability & Cost AMD MI300X Memory-bound large LLMs Tooling maturity Google Cloud TPUs Scaling MoE/Reasoning Less 'plug-and-play' than CUDA AWS Inferentia2 Cost-optimized serving Neuron ecosystem lock-in Intel Gaudi 3 Ethernet-first scale-out Smaller ecosystem The Memory Bottleneck For most transformer-based LLMs, the real bottleneck isn't just compute—it’s memory…