The silicon, cloud, and systems powering intelligence
Behind every AI breakthrough is an infrastructure story. Training GPT-4 required thousands of GPUs running for months. Serving billions of inference requests needs distributed systems that rival the complexity of global internet infrastructure.
The AI infrastructure stack spans from custom silicon chips (NVIDIA H100, Google TPU, AWS Trainium) through distributed training frameworks (PyTorch, DeepSpeed, Ray) to inference optimization (vLLM, TensorRT) and cloud platforms (AWS Bedrock, Azure AI, GCP Vertex).
NVIDIA H100/B200 GPUs dominate training with massive tensor cores. Google designs TPUs, AWS builds Trainium/Inferentia, and startups like Cerebras and Groq push novel architectures.
Models too large for one GPU use data parallelism (split batches), model parallelism (split layers), and pipeline parallelism (split stages). Frameworks like DeepSpeed and FSDP manage this.
Clusters of thousands of GPUs connected by high-bandwidth networks (NVLink, InfiniBand). Training runs are orchestrated by schedulers like Kubernetes + Ray.
vLLM uses PagedAttention for efficient memory. TensorRT compiles models for GPU. Quantization (INT8, INT4) shrinks models. Speculative decoding speeds up generation.
Load balancers distribute requests. Model replicas handle throughput. KV-cache optimization reduces redundant computation. Batching groups requests for efficiency.
AWS Bedrock, Azure AI, GCP Vertex abstract away infrastructure - pay-per-token APIs let you use frontier models without managing GPUs.
H100, B200, GB200 NVL72 - the gold standard for AI compute
v5p, Trillium - custom ASICs optimized for transformer workloads
Trainium2 for training, Inferentia2 for inference - 40% cheaper
Training frameworks - PyTorch dominates research, JAX powers Google
Inference engines - PagedAttention, continuous batching, streaming
Distributed orchestration - scale training and serving across clusters
Controls ~90% of AI training hardware. CUDA ecosystem is the moat.
Bedrock (managed models), SageMaker (custom training), Trainium (custom chips)
TPU pods for massive training, Vertex AI for deployment, Gemini API
LPU (Language Processing Unit) - inference at 500+ tokens/sec
Key Takeaway
AI infrastructure is the new cloud computing. Whoever controls the compute, controls the AI. The stack is shifting from 'rent GPUs' to 'consume intelligence as a service.'