AMD
Advanced Micro Devices, the second-largest supplier of GPUs for AI workloads, competing with NVIDIA through its Instinct MI series of data center accelerators.
AMD's MI300X GPU launched in late 2023 to compete with NVIDIA's H100, offering 192GB of HBM3 memory versus H100's 80GB. AMD projected $4.5 billion in data center GPU revenue for 2024, growing rapidly but still a fraction of NVIDIA's dominance. Major cloud providers including Microsoft Azure and Oracle Cloud have adopted MI300X for AI workloads. AMD's ROCm software ecosystem is expanding but trails NVIDIA's CUDA in maturity and developer adoption. The company represents the most credible competitive alternative to NVIDIA's AI chip dominance.
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Related Terms
AI Compute
The computational resources — primarily GPU and TPU processing power — required to train and run AI models, typically measured in FLOP (floating-point operations) or GPU-hours.
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
Data Center
A facility housing computer systems and infrastructure used to process, store, and distribute data — increasingly built specifically for AI training and inference workloads.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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