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AI‑Accelerator Landscape (2025)

Category Chip / Family Key Designer Typical Use‑Case Notable Specs (2024‑25) When to Pick It
ASICs (single‑purpose) Google TPU‑v4 Google Cloud‑scale inference & training (TensorFlow, XLA) 5 TFLOP/s (FP16) per core, 8 GB HBM3, 400 W per chip Large‑scale ML data centers that run the same model over and over.
Graphcore IPU‑700M Graphcore High‑performance ML training, graph‑based workloads 700 TFLOP/s (int8), 4 GB HBM3, 800 W per 8‑chip “pod” Research clusters, hyper‑parameter search, graph‑intensive models (e.g., GNNs).
Habana Gaudi 2 Habana Labs (Intel) Data‑center inference & training 200 TFLOP/s (int8), 32 GB HBM3, 300 W per chip Edge‑to‑cloud inference, cost‑efficient training.
Cerebras WSE‑2 Cerebras Systems Extreme‑scale training 400 TFLOP/s (FP32) on 1 wafer, 4.3 TB L1/2 memory, 8 kW Training the biggest models (hundreds of billions of params).
Myriad X / X2 Intel Edge inference (vision, audio) 1 TFLOP/s (FP16), 1.2 GB LPDDR4, <1 W Mobile/robotics, IoT, automotive.
Apple Neural Engine (ANE) Apple On‑device inference (iOS, macOS) ~350 GFLOP/s (int8), 1.5 W Mobile ML, AR/VR, Face ID.
Qualcomm Snapdragon Neural Processing Engine Qualcomm Mobile inference ~300 GFLOP/s (int8), ~1.5 W Smartphones, wearables, automotive infotainment.
NVIDIA A100 NVIDIA (GPU with ASIC‑style tensor cores) Mixed training & inference 312 TFLOP/s (FP16), 40 GB HBM3, 400 W Cloud training, AI inference, HPC workloads.
GPUs (massively parallel) NVIDIA H100 NVIDIA Training & inference, HPC, AI workloads 80 TFLOP/s (FP16), 80 GB HBM3, 700 W AI research, generative models, large‑scale inference.
AMD MI300X AMD Training & inference 140 TFLOP/s (FP16), 96 GB HBM3, 800 W HPC clusters, AI training, scientific simulation.
NVIDIA RTX 4090 NVIDIA High‑end gaming & inference 35 TFLOP/s (FP16), 48 GB GDDR6X, 450 W Edge inference, content creation, small‑scale training.
Apple M1/M2 Pro/Max Apple Edge inference + ML compute (macOS) 35 GFLOP/s (int8) per chip, <30 W Desktop ML, video analytics, AR.
FPGA‑based Xilinx Alveo U55C Xilinx (AMD) Customizable inference pipelines 1‑3 TFLOP/s (int8), 64 GB DDR5, 1.5 kW Low‑latency inference in telecom, finance.
Intel Stratix 10 Intel Custom ML accelerators 10‑20 TFLOP/s (int8) per card, 2 kW Research, ASIC prototyping, edge.
Hybrid / “Programmable ASIC” Intel Gaudi 2 (ASIC‑like, but programmable via XLA) Intel Cloud inference, training 200 TFLOP/s (int8), 32 GB HBM3, 300 W Middle‑tier inference, cost‑efficient scaling.
AMD Instinct MI200 AMD HPC + AI workloads 55 TFLOP/s (FP32), 32 GB HBM2, 700 W Scientific simulation with ML components.
NVIDIA Ampere & Hopper NVIDIA Training & inference 312 TFLOP/s (FP16) on A100, 80 TFLOP/s on H100 General AI research & production.

How to Pick the Right Chip

Decision Factor Best Match
Model size & training needs Cerebras WSE‑2, Graphcore IPU‑700M (for > 10‑B parameter models)
Inference‑only, low‑power edge Apple ANE, Qualcomm Neural Engine, Myriad X
Large‑scale data‑center training NVIDIA H100, AMD MI300X
GPU‑friendly research / mixed workloads NVIDIA A100, AMD Instinct
Cost‑sensitive inference at scale Habana Gaudi 2, Intel Gaudi 2
Custom, low‑latency pipelines Xilinx Alveo, Intel Stratix
Gaming / consumer GPUs that can double as inference NVIDIA RTX 4090, AMD Radeon RX 7900 XTX

Quick Reference: What Makes Each Category Shine

Category Strength Weakness
ASIC Max throughput per watt, minimal latency, zero software overhead Fixed, expensive to develop, no flexibility
GPU Massive parallelism, strong ecosystem (CUDA, OpenCL), good for both training & inference Slightly higher power draw, less efficient for small, deterministic workloads
FPGA Programmable hardware, low latency, good for custom data paths Slower development, higher per‑Watt overhead than ASIC, limited floating‑point performance
Hybrid Combines ASIC performance with a touch of programmability Still more costly than pure GPU, may not match the absolute efficiency of a true ASIC

Emerging & Hot‑Topics (2025)

  1. Chip‑on‑Chip (CoC) Fabrication – Intel’s “CoC” approach stacks multiple logic layers, potentially reducing silicon area and power.
  2. Neuromorphic Processors – Intel Loihi 2, BrainChip Akida; targeted at spiking‑neuron inference for low‑power edge tasks.
  3. Quantum‑Hybrid Accelerators – IBM, Google, and startups exploring QPU + classical co‑processor combos for near‑term ML workloads.
  4. Optical & Photonic Accel. – Silicon photonics chips for matrix multiplication (e.g., Lightelligence, Lightmatter) – early‑stage but promising for latency‑critical inference.

Bottom‑Line Takeaway

  • If performance‑per‑watt for a fixed workload is your goal, choose a purpose‑built ASIC (TPU‑v4, Gaudi, Cerebras).
  • If flexibility and rapid development are more important, go with a GPU (H100, MI300) or a programmable FPGA.
  • For edge devices with tight power budgets, lean on Apple/Qualcomm/Mobile‑specific ASICs.

Keep an eye on the ecosystem—software support (TensorFlow, PyTorch, ONNX, CUDA, ROCm) and vendor tooling can often outweigh raw silicon numbers when you factor in engineering effort and time‑to‑market. Happy chip hunting!

November 27, 2025 ∙