Inference Chip

An inference chip is a specialized semiconductor processor optimized for efficiently running AI models during inference. Unlike general-purpose CPUs or training-optimized GPUs, inference chips prioritize throughput (TPS), energy efficiency, and low latency for already-trained models. The three dominant categories: GPUs like NVIDIA's H100 and B200 Blackwell, excelling through massive parallel compute and specialized Tensor Cores; TPUs (Tensor Processing Units) from Google, purpose-built for matrix multiplications in neural networks; and ASICs (Application-Specific Integrated Circuits) for single-task optimization — including Groq's LPU achieving 500+ TPS, Cerebras' CS-3, and Amazon's Inferentia chips. NVIDIA's Blackwell generation (GB200, B200) has reshaped the inference landscape: native FP4 enables 4× more operations per watt versus H100; 192GB HBM3e memory holds even the largest frontier models entirely in VRAM. The GB200 NVL72 rack (72 B200 GPUs, 1.4TB total VRAM) achieves 30× higher throughput than H100 systems. The right chip selection profoundly influences cost, latency, and maximum model size. Smaller models run efficiently on single H100s; frontier models require multi-GPU clusters with hundreds of accelerators. As model quantization (FP4, INT8) becomes standard, ASICs increasingly outperform GPUs for fixed-workload inference at dramatically lower power.