AI Chip Providers Comparison
Compare AI chip and accelerator providers - GPU/TPU performance, power efficiency, memory, software ecosystem, and pricing.
Last updated: 2025-05-01
| Feature |  Nvidia B200Nvidia |  AMD MI300XAMD |  Intel Gaudi 3Intel |  Google TPU v5pGoogle |  Apple M4 UltraApple |  Qualcomm Cloud AI 100Qualcomm |  Cerebras WSE-3Cerebras |
|---|---|---|---|---|---|---|---|
| General | |||||||
| Headquarters | Santa Clara, CA | Santa Clara, CA | Santa Clara, CA | Mountain View, CA | Cupertino, CA | San Diego, CA | Sunnyvale, CA |
| Founded | 1993 | 1969 | 1968 | 1998 | 1976 | 1985 | 2016 |
| Company Type | Public (NASDAQ: NVDA) | Public (NASDAQ: AMD) | Public (NASDAQ: INTC) | Public (NASDAQ: GOOGL) | Public (NASDAQ: AAPL) | Public (NASDAQ: QCOM) | Private (~$4B valuation) |
| Market Cap (Approx.)(?) | ~$2.8T+ | ~$200B+ | ~$90B | ~$2.2T+ | ~$3.5T+ | ~$190B+ | ~$4B (private valuation) |
| Primary AI Focus | Data center training & inference GPUs | Data center GPUs & CPUs | AI accelerators & CPUs | Cloud TPU accelerators | On-device Neural Engine | Edge & mobile AI inference | Wafer-scale AI training |
| Latest AI Chip Specifications | |||||||
| Latest AI Chip | B200 (Blackwell) | Instinct MI300X | Gaudi 3 | TPU v5p | M4 Ultra (Neural Engine) | Cloud AI 100 Ultra | WSE-3 (Wafer-Scale Engine 3) |
| Architecture | Blackwell | CDNA 3 | Habana Labs custom | Custom ASIC (SparseCore + MXU) | Apple Silicon (Neural Engine 16-core) | Kryo + Hexagon NPU | Wafer-Scale Engine |
| Process Node | TSMC 4NP (4nm) | TSMC 5nm + 6nm (chiplet) | TSMC 5nm | Custom (not publicly disclosed) | TSMC 3nm (N3B) | TSMC 7nm (Samsung 4nm for Snapdragon) | TSMC 5nm |
| Transistor Count | 208 billion | 153 billion (combined chiplets) | Not disclosed | Not publicly disclosed | Not disclosed (M4 Ultra est. ~50B+) | Not disclosed | 4 trillion (wafer-scale) |
| Die Size | 814 mm² | Multiple chiplets (total ~750 mm²) | Not disclosed | Not disclosed | Not disclosed | Not disclosed | 46,225 mm² (full wafer) |
| Chip Type | GPU | GPU (chiplet design) | ASIC (AI accelerator) | ASIC (TPU) | SoC (integrated Neural Engine) | ASIC / SoC | Wafer-scale ASIC |
| AI Performance | |||||||
| FP8 Performance (Training)(?) | 9 PFLOPS (per GPU) | 2.6 PFLOPS | 1.835 PFLOPS | 459 TFLOPS per chip | N/A (not designed for training) | N/A | 125 PFLOPS (per WSE-3 system) |
| FP16 / BF16 Performance(?) | 4.5 PFLOPS | 1.3 PFLOPS | 1.835 PFLOPS (BF16) | 459 TFLOPS (BF16 per chip) | ~27 TFLOPS (GPU portion of M4 Ultra) | ~400 TOPS (INT8 optimized) | 62 PFLOPS |
| INT8 Inference Performance(?) | 18 PFLOPS | 5.2 PFLOPS | 3.67 PFLOPS | ~918 TOPS per chip | 38 TOPS (Neural Engine) | 400 TOPS | 250 PFLOPS |
| FP4 Performance(?) | 18 PFLOPS | Not supported (MI300X gen) | Not supported | Not disclosed | Not supported | Not supported | Not disclosed |
| Sparsity Support(?) | |||||||
| Key Use Case | Training + Inference (data center) | Training + Inference (data center) | Training + Inference (data center) | Training + Inference (Google Cloud) | On-device inference (mobile/desktop) | Edge inference + mobile AI | Large-scale training (data center) |
| Memory Specifications | |||||||
| Memory Type | HBM3e | HBM3 | HBM2e | HBM (integrated on-package) | Unified Memory (LPDDR5X) | LPDDR5X | On-chip SRAM (44 GB) |
| Memory Capacity | 192 GB HBM3e | 192 GB HBM3 | 128 GB HBM2e | 95 GB HBM per chip | Up to 192 GB unified memory | Up to 128 GB (system LPDDR5X) | 44 GB SRAM (on-chip) |
| Memory Bandwidth | 8 TB/s | 5.3 TB/s | 3.7 TB/s | 4.8 TB/s per chip | ~800 GB/s (unified memory) | ~134 GB/s | 21 PB/s (on-chip SRAM bandwidth) |
| ECC Memory Support | |||||||
| Power & Efficiency | |||||||
| TDP / Power Consumption(?) | 1,000W | 750W | 900W | ~250-300W per chip (estimated) | ~60W (entire M4 Ultra SoC) | 75W (Cloud AI 100 Ultra) | ~23,000W (full CS-3 system) |
| Performance per Watt (FP16)(?) | ~4.5 TFLOPS/W | ~1.7 TFLOPS/W | ~2.0 TFLOPS/W | ~1.5-1.8 TFLOPS/W (estimated) | ~0.45 TFLOPS/W | ~5.3 TOPS/W (INT8 optimized) | ~2.7 TFLOPS/W |
| Cooling Requirement | Liquid cooling recommended | Liquid cooling recommended | Air or liquid cooling | Custom Google DC cooling | Passive / fan (consumer) | Air cooled (fanless possible) | Custom liquid cooling (CS-3) |
| Software Ecosystem | |||||||
| Primary AI Framework | CUDA / cuDNN | ROCm / HIP | oneAPI / Habana SynapseAI | JAX / TensorFlow (XLA) | Core ML / MLX | Qualcomm AI Engine / SNPE | Cerebras Software Platform (CSoft) |
| PyTorch Support | Via MLX (PyTorch-like API) | Partial (ONNX export) | |||||
| TensorFlow Support | Via Core ML conversion | Via ONNX / TFLite | |||||
| JAX Support | Experimental | ||||||
| Ecosystem Maturity(?) | Industry-leading (CUDA dominance) | Maturing (ROCm catching up) | Developing (Gaudi ecosystem growing) | Mature (for Google Cloud users) | Growing (MLX gaining traction) | Niche (edge/mobile focused) | Specialized (wafer-scale focused) |
| Developer Community Size(?) | Largest (millions of CUDA developers) | Growing (~100K+ ROCm developers) | Moderate | Large (GCP/TensorFlow community) | Large (iOS/macOS developers) | Moderate (mobile developers) | Small (specialized HPC/AI) |
| Interconnect & Scalability | |||||||
| Chip-to-Chip Interconnect | NVLink 5 (1.8 TB/s bidirectional) | Infinity Fabric (896 GB/s) | Intel on-package interconnect | ICI (Inter-Chip Interconnect) | UltraFusion (2.5 TB/s die-to-die) | N/A (standalone accelerator) | SwarmX fabric |
| Multi-Node Networking | NVLink Switch + InfiniBand / Ethernet | Infinity Fabric + RoCE / InfiniBand | Ethernet (Gaudi integrated RoCE) | ICI 3D torus topology (up to 8960 chips) | Thunderbolt / Not designed for clusters | PCIe / Ethernet | MemoryX + SwarmX (up to 2048 CS-3s) |
| Max GPU/Chip Cluster Scale(?) | 576 GPUs (GB200 NVL72 superpod x8) | Thousands (via InfiniBand) | 4096 Gaudi 3 (SuperPod equivalent) | 8,960 chips (TPU v5p pod) | Single machine only | Rack-scale (8-16 cards) | 2,048 CS-3 systems (Condor Galaxy) |
| PCIe Interface | PCIe 5.0 x16 | PCIe 5.0 x16 | PCIe 5.0 x16 | N/A (custom interconnect) | N/A (integrated SoC) | PCIe 4.0 x16 | Custom (SwarmX interface) |
| Cloud Availability | |||||||
| AWS | |||||||
| Google Cloud (GCP) | |||||||
| Microsoft Azure | |||||||
| Oracle Cloud (OCI) | |||||||
| CoreWeave / GPU Clouds | Limited | ||||||
| On-Premise / Purchasable | |||||||
| Pricing | |||||||
| Chip / Card MSRP(?) | ~$30,000-$40,000 (B200 estimated) | ~$10,000-$15,000 | ~$15,000-$20,000 (estimated) | Not sold (cloud-only) | $3,999-$7,999 (Mac Studio w/ M4 Ultra) | ~$5,000-$15,000 (Cloud AI 100 cards) | ~$2-3M per CS-3 system |
| Cloud Instance Pricing (per hr)(?) | $2-$4/hr (H100), ~$5-8/hr (B200 est.) | ~$1.50-$3.00/hr (MI300X Azure) | ~$3.50/hr (Gaudi 2 on AWS; Gaudi 3 TBD) | ~$3.22/hr (TPU v5p per chip) | N/A (no cloud offering) | N/A (mostly edge deployment) | Custom pricing (contact sales) |
| Price-Performance Ratio(?) | Premium (best performance, highest cost) | Value (strong performance, lower cost) | Competitive (targeting cost-sensitive buyers) | Competitive (for GCP workloads) | Best value for on-device AI | Best value for edge inference | Premium (specialized large-model training) |
| Next Generation (Upcoming) | |||||||
| Next-Gen Chip | B300 / GB300 (Blackwell Ultra, H2 2025) | MI350X (CDNA 4, late 2025) | Gaudi 4 (Falcon Shores, 2025-2026) | TPU v6e (Trillium, 2025) | M5 Ultra (Neural Engine, 2025-2026) | Next-gen Cloud AI (2025-2026) | WSE-4 (expected 2026) |
| Expected Improvement | ~1.5x inference over B200, FP4 native | ~3.5x AI inference over MI300X | Unified GPU + accelerator architecture | ~4.7x training throughput improvement over v5e | Improved Neural Engine, 3nm enhanced | Higher INT8 efficiency, edge AI focus | Larger wafer, higher transistor density |
| Process Node (Next Gen) | TSMC 4NP enhanced | TSMC 3nm | Intel 18A / TSMC 3nm | Not disclosed | TSMC N3E / N2 | TSMC 3nm or Samsung 3nm | TSMC 3nm (expected) |