AI Accelerators Analysis
The US dominates AI accelerator production through NVIDIA's near-monopoly on training GPUs. Export controls have severely limited China's access to cutting-edge chips, though domestic alternatives are emerging.
Key Metrics
Effective FLOPs ≈ (Units shipped) × (Peak FLOPs) × (Realized MFU)
Throughput is limited by min(Compute, Memory bandwidth, Interconnect, Power)
What matters in this layer
Dominance comes from the full stack: architectures, compilers, supply contracts, and the ability to scale systems. Export controls and allocation policies can redirect global compute flows.
Accelerators are the intersection of leading fabs, advanced packaging, and HBM. A constraint in any upstream layer appears here as shipping delays.
Compilers, kernels, and libraries determine real MFU. Software ecosystems are sticky and convert hardware advantage into durable platform power.
Below is a first‑principles embedded module that turns “Effective FLOPs produced” into a compact, scroll‑driven comparison.
Effective FLOPs produced (indexed)
Scroll within this block to fill the bars. Scale is indexed so the layout is stable while you finalize numbers.
NVIDIA's H100 Dominates AI Training
NVIDIA's H100 GPU continues to be the gold standard for large-scale AI model training, with US-based hyperscalers deploying hundreds of thousands of units in their data centers. The company controls over 80% of the AI training chip market.
Export Controls Limit Chinese Access to Advanced GPUs
US export controls have effectively blocked Chinese entities from acquiring the most advanced AI accelerators, forcing domestic alternatives that lag 2-3 generations behind in performance and efficiency.
Huawei's Ascend 910C Gains Traction
Despite sanctions, Huawei has developed the Ascend 910C AI accelerator using older process nodes. While performance remains below cutting-edge US chips, domestic adoption is growing among Chinese AI labs.
NVIDIA Blackwell Architecture Ships
NVIDIA has begun shipping its next-generation Blackwell architecture GPUs, offering 4x the training performance of H100 for large language models. Demand far exceeds supply.
Google TPU v5 Powers Gemini Models
Google has deployed its fifth-generation Tensor Processing Units (TPUs) across its data centers, optimized specifically for training and serving large language models like Gemini.