What is the difference between a GPU and a TPU for AI?

GPUs are general-purpose parallel processors with thousands of cores and tensor cores for matrix math — flexible, programmable, and dominant for training and research. TPUs use systolic array architecture specifically optimised for Google's AI workloads — more energy-efficient for stable, large-scale inference but tightly integrated with Google's XLA/JAX software stack and only available through Google Cloud. GPUs win for flexibility; TPUs win for Google-scale cost efficiency.

What is an NPU and why do phones have one?

A Neural Processing Unit is a dedicated AI inference accelerator built into mobile and laptop chips. Unlike a GPU, which is designed for flexible parallel compute, an NPU is optimised for the specific mathematical operations in neural networks (matrix multiply, convolutions, activation functions) while consuming a fraction of the power. Apple's Neural Engine, Qualcomm's Hexagon, and Google's Tensor NPU enable real-time AI features (face recognition, translation, computational photography) on-device without cloud connectivity or battery drain.

What makes Groq's LPU different from a GPU for inference?

Groq's Language Processing Unit uses a deterministic dataflow architecture: the model is compiled at deployment time into a fixed execution schedule, eliminating the runtime scheduling overhead that causes GPU inference latency to vary. The result is extremely consistent, high-speed inference — benchmarks show over 750 tokens/second for Mixtral 8x7B, roughly 10× faster than GPU inference. The tradeoff is inflexibility: models must be compiled for Groq's architecture, and dynamic control flow in models is constrained.

Why is memory bandwidth more important than FLOPS for AI hardware?

Most AI operations are memory-bandwidth limited, not compute-limited. A GPU can perform matrix multiplications faster than it can load the data from memory. During LLM inference, the bottleneck is streaming billions of model weight parameters from HBM memory into compute units on every token generated. More FLOPS does not help if the memory cannot feed them fast enough. This is why HBM3E (8 TB/s on NVIDIA B200) and the AMD MI300X's 192 GB HBM3 pool matter more than raw compute numbers for many workloads.

Should I use a GPU or ASIC for AI inference in production?

GPUs are the right choice for most teams: they support any model, any framework, and any precision without recompilation, and NVIDIA's software ecosystem (CUDA, TensorRT, vLLM) is mature and optimised. ASICs like Groq LPU make sense when you have a stable, high-throughput inference workload where latency and cost-per-token dominate, and you can afford the integration cost of compiling your model for the ASIC's architecture. Hyperscalers (Google, Amazon, Microsoft) build custom ASICs because at their scale, even 15% efficiency gains are worth $500M+ in development costs.

AI & Automation·11 min read·June 19, 2026

⚡ GPUs, TPUs, NPUs, and ASICs: The Silicon Powering Modern AI Systems

A systems engineer's guide to AI accelerator hardware: why CPUs fail at AI workloads, how GPU tensor cores work, when to use a TPU vs GPU, what NPUs do on your phone, how custom ASICs like Groq LPU and Cerebras WSE differ, and why memory bandwidth is often the real bottleneck.

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Why CPUs Cannot Keep Up with AI Workloads

The question of why AI needs special hardware has a precise answer. At the heart of every neural network is one operation: matrix multiplication. A transformer layer performing attention computes Q×K^T, applies softmax, multiplies by V, and passes through a feed-forward network — with matrix operations constituting roughly 90% of total compute time.

CPUs are built for the opposite of what this requires. A CPU is optimised for branch prediction, cache coherency, and low-latency single-thread execution — capabilities that are irrelevant to dense matrix math. A modern Intel Xeon might run 64 threads. An NVIDIA H100 GPU runs 16,896 CUDA cores, plus tensor cores that perform entire 16×16 matrix multiply-accumulate operations per cycle. Same matrix multiplication, orders of magnitude faster on GPU:<

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AI hardwareGPU AITPU architectureNPU chipASIC AINVIDIA tensor coresGroq LPUCerebras WSEHBM memory bandwidthAI acceleratorCUDA GPUApple Neural EngineQualcomm Hexagonsystolic arraytraining vs inference hardwareHBM2e HBM3GPU VRAMAI silicondeep learning hardwareAI chip comparison