AI Chips & Hardware
Custom silicon for AI workloads — from NVIDIA GPUs to custom ASICs by Cerebras, Groq, and others. The infrastructure layer powering the AI revolution.
Why It Matters in 2026
The AI hardware market is experiencing unprecedented demand and innovation. NVIDIA's dominance is being challenged by custom silicon from Cerebras, Groq, AMD, and cloud providers building their own chips.
In 2026, the bottleneck for AI progress is compute, not algorithms. Companies that can deliver faster, cheaper inference and training at scale hold enormous strategic value.
New architectures — from wafer-scale chips to photonic computing — are pushing the boundaries of what's physically possible. The hardware layer is where the next order-of-magnitude improvements in AI performance will come from.
Key Companies
🇺🇸CoreWeave
AI Infrastructure
$42.0B
🇺🇸Lambda Labs
AI Infrastructure
$4.0B
🇺🇸Fireworks AI
AI Infrastructure
$4.0B
🇺🇸Groq
AI Infrastructure
$2.8B
🇨🇦Tenstorrent
AI Infrastructure
$3.2B
🇺🇸Baseten
AI Infrastructure
$5.0B
🇺🇸Cerebras
AI Infrastructure
$4.0B
🇺🇸Together AI
AI Infrastructure
$1.3B
🇺🇸Modular AI
AI Infrastructure
$1.6B
🇺🇸Replicate
AI Infrastructure
$350M
Related Trends
Open Source AI
Open-weight models from Meta (Llama), Mistral, and others that anyone can download, modify, and deploy. Democratizing access to frontier AI.
🏛️Sovereign AI
Countries building their own AI infrastructure, models, and data centers to ensure digital sovereignty and reduce dependence on US tech giants.
🦾Humanoid Robots & AI Robotics
Physical AI systems — humanoid robots from Figure, Boston Dynamics, and 1X Technologies that can navigate real-world environments.
Frequently Asked Questions
Why is AI hardware important?
AI workloads require massive parallel computation. Specialized hardware (GPUs, TPUs, custom ASICs) determines the speed, cost, and energy efficiency of training and deploying AI models.
Does NVIDIA dominate AI chips?
NVIDIA holds approximately 80% of the AI training chip market with its H100 and B200 GPUs. However, AMD, Intel, and startups like Cerebras and Groq are competing aggressively.
What are AI inference chips?
Inference chips are optimized for running trained models rather than training them. Companies like Groq, SambaNova, and cloud providers are building specialized inference hardware for faster, cheaper AI deployment.
How much does AI compute cost?
Training a frontier model can cost $100M+. Inference costs vary widely but are dropping rapidly. Cloud GPU instances range from $1-$30/hour depending on the chip and provider.