AI Chip Providers Comparison
Compare AI chip and accelerator providers - GPU/TPU performance, power efficiency, memory, software ecosystem, and pricing.
TL;DR
Comparing Nvidia B200, AMD MI300X, Intel Gaudi 3, Google TPU v5p, Apple M4 Ultra, Qualcomm Cloud AI 100, Cerebras WSE-3 across 46 features in 10 categories.
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| Feature |  Nvidia B200 |  AMD MI300X |  Intel Gaudi 3 |  Google TPU v5p |  Apple M4 Ultra |  Qualcomm Cloud AI 100 |  Cerebras WSE-3 |
|---|---|---|---|---|---|---|---|
| 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) |
Frequently Asked Questions
What is the difference between Nvidia B200 and AMD MI300X?
Nvidia B200 and AMD MI300X are both leading tools in this category but serve different use cases. Our comparison breaks down their differences across performance, pricing, reliability, and ease of use — so you can pick the right one for your workflow.
Which is better: Nvidia B200 or AMD MI300X?
The answer depends on your use case. Nvidia B200 typically excels for users who prioritise ecosystem integrations and ease of onboarding. AMD MI300X tends to lead on performance depth. See our full score breakdown and "choose if" guide above for a definitive recommendation.
How is We Compare AI's comparison data collected?
All data is collected independently by our team of AI specialists using a standardised benchmark methodology. We test each tool directly, track public pricing from official sources, and update scores when models release significant updates. No vendor pays to appear or influence their ranking.
How does Nvidia B200 compare to Intel Gaudi 3?
Nvidia B200 and Intel Gaudi 3 target overlapping use cases but differ in pricing models and feature sets. Our comparison table above includes Intel Gaudi 3 alongside Nvidia B200 and AMD MI300X so you can evaluate all options side by side.
Is there a free version of Nvidia B200?
Most major AI tools including Nvidia B200 offer a free tier with usage limits. Check our pricing comparison above for exact plan details, token limits, and cost-per-million-token breakdowns for Nvidia B200, AMD MI300X, Intel Gaudi 3, Google TPU v5p, Apple M4 Ultra, Qualcomm Cloud AI 100, Cerebras WSE-3.
Last updated: 2025-05-01 · How we collect data →