Groq
An AI chip company that designs custom Language Processing Units (LPUs) for ultra-fast AI inference, offering the fastest commercially available token generation speeds for large language models.
Groq's LPU chips can generate tokens at 500+ tokens per second, approximately 10x faster than GPU-based inference. The company's deterministic architecture eliminates the memory bottleneck that limits GPU inference speeds. Founded by a former Google TPU architect, Groq has raised over $640 million. The company offers cloud-based API access to models like Llama and Mixtral running on its LPU hardware. Groq represents a growing class of companies designing specialized AI chips optimized for inference rather than training workloads.
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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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