Semiconductor Type:
GPUs
GPUs are massively parallel processors originally designed for graphics but now used broadly across scientific computing, visualization, and machine learning. They feature thousands of compute cores, high-bandwidth memory, and specialized matrix engines for mixed-precision math. While AI-specific accelerators target narrow workloads, GPUs remain the most versatile parallel processors for training, inference, rendering, and simulation.
Role in the Semiconductor Ecosystem
- Provide high-throughput parallel compute for linear algebra, rendering, and simulation.
- Anchor AI clusters alongside CPUs and high-bandwidth interconnects.
- Drive innovation in advanced packaging, chiplets, and HBM memory stacks.
- Benefit from mature software ecosystems (CUDA, ROCm, DirectX/Vulkan, OpenCL).
GPU Architecture Building Blocks
- Streaming Multiprocessors (SM/CU): SIMD/SIMT cores organized into arrays for parallel workloads.
- Matrix/Tensor Engines: Specialized units for FP16/FP8/INT8 operations to accelerate AI.
- Memory Subsystem: High Bandwidth Memory (HBM) or GDDR with wide buses and large caches.
- Interconnect: NVLink/Infinity Fabric/PCIe/CXL for multi-GPU scaling and CPU coherency.
- Graphics Pipeline: Fixed-function units (raster, RT cores) for rendering and ray tracing.
Representative Vendors & Platforms
| Vendor | Datacenter GPU Families | Client/Workstation GPU Families | Software Ecosystem | Notes |
|---|---|---|---|---|
| NVIDIA | A100/H100/B100-class (training & inference) | GeForce RTX, RTX Professional | CUDA, cuDNN, TensorRT, NCCL, Omniverse | Dominant in AI training; strong NVLink/NVSwitch scaling |
| AMD | Instinct MI series (training & inference) | Radeon RX/PRO | ROCm, MIOpen, HIP, Infinity Fabric | Open ecosystem focus; competitive HBM bandwidth |
| Intel | Data Center GPU (Flex/Max) | Arc Alchemist/Pro | oneAPI, SYCL/DPC++, OpenVINO | Emphasis on open standards and CPU+GPU synergy |
Primary Use Cases
- AI/ML Training: Large-scale distributed training with mixed precision and fast interconnects.
- AI Inference: Batch and real-time inference where latency and throughput balance is needed.
- HPC & Scientific: CFD, molecular dynamics, weather, finance risk modeling.
- Graphics & Visualization: Game engines, DCC, CAD/CAE, path/ray tracing, virtual production.
- Digital Twins & Simulation: Robotics, factories, autonomous systems, and synthetic data generation.
Cluster Design Considerations
- Interconnect Topology: NVLink/Infinity Fabric/NVSwitch vs PCIe-only impacts scale efficiency.
- Memory Capacity & Bandwidth: HBM capacity (per GPU) and aggregate bandwidth govern model size and speed.
- CPU Balance: Sufficient CPU cores and DRAM to feed GPUs; NUMA and PCIe lane planning.
- Storage & I/O: Parallel file systems (Lustre, GPFS), NVMe fabrics, and data staging pipelines.
- Thermals & Power: Liquid cooling and high-density racks for multi-kW nodes.
Software & Ecosystem
- Frameworks: PyTorch, TensorFlow, JAX integrated with vendor libraries (cuDNN, ROCm, oneAPI).
- Compilers & Runtimes: CUDA toolchain, HIP/ROCm, SYCL/oneAPI, Triton kernels.
- Scheduling: Kubernetes/Slurm with GPU operators, MIG/SR-IOV partitioning.
- Libraries: NCCL (collectives), cuBLAS/rocBLAS, cuSPARSE/rocSPARSE, graph analytics.
Selection Guide (GPU vs AI Accelerator)
- Choose GPU for broad workloads, strong ecosystem support, and mixed graphics + compute needs.
- Choose AI Accelerator for specific AI workloads where higher perf/watt or lower latency is provable.
- Consider hybrid clusters (CPU+GPU+Accelerator) when workloads bifurcate between training and ultra-low-latency inference.
KPIs to Track
| KPI | What It Indicates | Why It Matters |
|---|---|---|
| TFLOPS/TFLOPS-FP8/INT8 | Raw compute throughput in mixed precision | Determines training/inference speed ceilings |
| HBM Bandwidth & Capacity | Memory speed and size per device | Bottleneck for large models and batch sizes |
| Interconnect Bandwidth | NVLink/IF/PCIe/CXL throughput | Scales multi-GPU efficiency and all-reduce ops |
| Perf/Watt | Energy efficiency at target workload | Impacts TCO and facility power budgets |
| Utilization | Percentage of time cores and memory are busy | Indicates pipeline balance and feeding efficiency |
Supply Chain & Market Considerations
- Advanced Nodes: Leading GPUs use cutting-edge process + HBM + advanced packaging; supply-limited at peaks.
- Software Lock-In: CUDA ecosystem advantage vs open alternatives (ROCm/oneAPI) affects portability.
- Export Controls: Datacenter GPUs can be subject to regional restrictions.
- Total Cost: Hardware acquisition, power, cooling, and datacenter integration dominate TCO.
Market Outlook
GPUs will remain the default high-throughput compute engines for AI training, graphics, and HPC, even as AI accelerators gain share in specialized niches. Expect continued advances in HBM capacity, interconnect bandwidth, and chiplet-based designs to push performance, while open software stacks work to reduce ecosystem lock-in.