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Infrastructure

Chips, inference, data centers, clusters, energy, and deployment economics.

Explore guide SourcesUpdated Sep 4, 4:37 AM

Data center roof with cooling infrastructure and backup systems

Infrastructure reading

Best technical references to scan

NVIDIA Technical BlogTensorRT-LLM and NVIDIA inference stack

CUDA/TensorRT patterns, model serving architecture, quantization, and GPU utilization ideas.

NVIDIA's technical posts are useful when infrastructure choices depend on GPU kernels, serving libraries, networking, and hardware-specific optimization.CoreWeave BlogModern AI research clusters with CoreWeave SUNK

Kubernetes-native cluster operations, scheduling, identity, observability, and long-running training workloads.

Good infrastructure coverage is not just GPUs; cluster lifecycle and reliability determine whether researchers can actually train at scale.Anyscale DocsServe LLMs with Anyscale services

Ray Serve, vLLM integration, production deployment shape, autoscaling, and serving endpoint architecture.

Ray is a core distributed-compute layer for many AI teams, so serving docs are a practical reference for production LLM deployment design.Together AI BlogBest practices to accelerate inference for large-scale production workloads

Kernel quality, memory bandwidth, FlashAttention-style optimization, FP8 paths, and production inference economics.

Inference cost and latency increasingly come from low-level kernel and memory behavior, not only model selection.Baseten BlogModel autoscaling features on Baseten

Replica scaling, scale-to-zero, cold starts, concurrency targets, and operational controls for model services.

Autoscaling is where AI products turn model demos into cost-controlled production services.Modal BlogHow we achieved truly serverless GPUs

Serverless GPU cold starts, container image loading, checkpoint/restore, and bursty inference workloads.

Serverless GPU design is becoming important as AI traffic gets more variable and teams need capacity without idle spend.Lambda Labs GitHubDistributed training guide

Practical multi-GPU and multi-node PyTorch training patterns, FSDP, tensor parallelism, launch, logging, and debugging.

Distributed training failures are often operational. A practical guide helps readers understand what breaks after a model outgrows one GPU.vLLM BlogInside vLLM: Anatomy of a high-throughput LLM inference system

Paged attention, scheduling, prefix caching, speculative decoding, multi-GPU serving, and throughput/latency tradeoffs.

vLLM is one of the most important open-source inference engines, so its architecture is a durable reference for serving modern LLMs.

Compute

Compute supply is now a strategic AI signal

Infrastructure coverage tracks chips, clusters, networking, inference serving, energy, cooling, and data-center buildouts because capacity constraints shape model releases and product pricing.

GPU allocations, custom accelerators, memory bandwidth, interconnects, and data-center power.

Inference

Inference cost can matter more than training headlines

Once models reach users, latency and cost per task decide whether a product can scale. Watch caching, distillation, quantization, batching, and small-model routing.

Benchmarks tied to real workloads, price cuts, hosted model catalogs, and edge deployment.

Energy

Power and cooling are becoming part of the AI roadmap

Major AI plans increasingly depend on grid access, cooling systems, siting, and long-term energy contracts, not only chips and algorithms.

Utility deals, data-center locations, regulatory friction, and efficiency claims.

Sources

Infrastructure source watchlist

NVIDIA Technical Blog CoreWeave Blog Anyscale Blog Together AI Blog Baseten Blog Modal Blog Lambda Blog Crusoe Blog Nebius Blog vLLM Blog