{"id":3215,"date":"2025-06-27T16:03:10","date_gmt":"2025-06-27T16:03:10","guid":{"rendered":"https:\/\/uplatz.com\/blog\/?p=3215"},"modified":"2025-07-01T17:00:00","modified_gmt":"2025-07-01T17:00:00","slug":"tpu-vs-gpu-googles-custom-chips-vs-traditional-accelerators","status":"publish","type":"post","link":"https:\/\/uplatz.com\/blog\/tpu-vs-gpu-googles-custom-chips-vs-traditional-accelerators\/","title":{"rendered":"TPU vs. GPU: Google’s Custom Chips vs. Traditional Accelerators"},"content":{"rendered":"

TPU vs. GPU: Google’s Custom Chips vs. Traditional Accelerators<\/b><\/h1>\n

Modern machine learning workloads demand high computational throughput and energy efficiency. Google\u2019s Tensor Processing Units (TPUs) and traditional Graphics Processing Units (GPUs) represent two distinct hardware approaches: TPUs are application-specific integrated circuits (ASICs) optimized for tensor operations, while GPUs are general-purpose parallel processors supporting a broad range of compute tasks. This report compares their architectures, performance characteristics, programming models, and ideal use cases.<\/span><\/p>\n

\"\"<\/p>\n

    \n
  1. Architectural Overview<\/b><\/li>\n<\/ol>\n

    1.1 TPU Architecture<\/b><\/p>\n

    TPUs leverage a <\/span>systolic array<\/b> design specialized for matrix multiplications. Each TPU v3 chip contains two TensorCores, each with two 128\u00d7128 matrix-multiply units (MXUs), vector units, and scalar units. TPUs use high-bandwidth memory (HBM2) to feed the MXUs, minimizing off-chip memory access during computation.<\/span>\u00a0TPU v4 advances this by offering two TensorCores per chip, with four MXUs each and HBM2 capacity of 32 GiB at 1200 GB\/s, interconnected in 3D mesh networks for pod deployment<\/span>.<\/span><\/p>\n

    1.2 GPU Architecture<\/b><\/p>\n

    GPUs such as NVIDIA\u2019s A100 are built on the Ampere architecture, comprising multiple <\/span>Streaming Multiprocessors (SMs)<\/b>. Each SM contains 64 FP32 CUDA cores and 4 third-generation Tensor Cores capable of 256 FP16\/FP32 fused-multiply-add (FMA) operations per clock. An A100 GPU has up to 108 SMs, HBM2e memory (40 GB at 1555 GB\/s), and supports NVLink and PCIe Gen4 interconnects for multi-GPU scaling<\/span>.<\/span><\/p>\n

      \n
    1. Performance Comparison<\/b><\/li>\n<\/ol>\n\n\n\n\n\n\n\n\n\n
      Metric<\/span><\/td>\nTPU v3 (per chip)<\/span><\/td>\nTPU v4 (per chip)<\/span><\/td>\nNVIDIA A100 (per GPU)<\/span><\/td>\n<\/tr>\n
      Peak Compute<\/span><\/td>\n123 TFLOPS (bfloat16)<\/span><\/td>\n275 TFLOPS (bfloat16\/int8)<\/span><\/td>\n19.5 TFLOPS (FP32)<\/span><\/td>\n<\/tr>\n
      High-Bandwidth Memory<\/span><\/td>\n32 GiB @ 900 GB\/s<\/span><\/td>\n32 GiB @ 1200 GB\/s<\/span><\/td>\n40 GB @ 1555 GB\/s<\/span><\/td>\n<\/tr>\n
      Tensor Cores<\/span><\/td>\n4 MXUs per chip<\/span><\/td>\n8 MXUs per chip<\/span><\/td>\n432 third-gen Tensor Cores<\/span><\/td>\n<\/tr>\n
      Power Consumption (mean)<\/span><\/td>\n220 W<\/span><\/td>\n170 W<\/span><\/td>\n250 W (PCIe) \/ 400 W (SXM)<\/span><\/td>\n<\/tr>\n
      Interconnect Topology<\/span><\/td>\n2D torus (v3 pods)<\/span><\/td>\n3D mesh\/torus (v4 pods)<\/span><\/td>\nNVLink 3.0 \/ PCIe Gen4<\/span><\/td>\n<\/tr>\n
      Pod Scale<\/span><\/td>\n1024 chips (126 PFLOPS)<\/span><\/td>\n4096 chips (1.1 EFLOPS)<\/span><\/td>\nMulti-GPU clusters via NVLink<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

       <\/p>\n

      TPUs excel in <\/span>dense tensor operations<\/b> and scale linearly in pod configurations, achieving up to 1.1 exaflops in TPU v4 pods<\/span>. The A100 offers versatile precision support (FP64, TF32, FP16, BF16, INT8) and excels in mixed workloads, with up to 624 TFLOPS FP16 performance when exploiting structured sparsity<\/span>.<\/span><\/p>\n

        \n
      1. Programming Models<\/b><\/li>\n<\/ol>\n

        3.1 TPU Programming<\/b><\/p>\n

        TPU programming centers on <\/span>TensorFlow<\/b> or JAX with XLA compilation. Models are compiled into HLO (High Level Optimizer) graphs, which XLA lowers to TPU executables. TPU pods use synchronous data parallelism via infeed queues and collective operations for all-reduce across cores. Careful batch sizing divisible by core count ensures high utilization<\/span>.<\/span><\/p>\n

        3.2 GPU Programming<\/b><\/p>\n

        GPUs follow a <\/span>heterogeneous<\/b> model with CUDA or ROCm. Developers write kernels executed on device SMs, organized into grids of thread blocks. Memory management (global, shared, constant) and explicit data transfers (e.g., <\/span>cudaMemcpy<\/span>) between host and device are key considerations. Libraries such as cuDNN and cuBLAS abstract low-level details for deep learning workloads<\/span>.<\/span><\/p>\n

          \n
        1. Cost and Energy Efficiency<\/b><\/li>\n<\/ol>\n