Arithmetic Intensity, Hardware Ceilings, Iteration-Level Continuous Batching, and the 2026 Production Serving Blueprint
*Technical Analysis & Systems Synthesis by *DistributedApps.ai | Synthesizing frontier research from DeepSeek, Moonshot AI, Zhipu AI, Alibaba, NVIDIA, and Google DeepMind into production engineering blueprints.
Free Edition: Comprehensive Chapter Roadmap
Below is the complete architectural roadmap covered in this chapter:
Foundational Physics: Breaking the Memory Wall & Arithmetic Intensity: Microarchitectural roofline modeling across NVIDIA Hopper H100 and Blackwell B200 accelerators.
The Memory Allocation Hierarchy & VRAM Capacity Bottleneck: Exact memory accounting for model weights, dynamic KV activations, and CUDA workspaces.
The Prefill-Decode Dichotomy & Streaming Multiprocessor Contention: Why high-arithmetic-intensity prompt prefill and memory-bound autoregressive decoding create execution bubbles when co-located.
Evolution of Batching Systems: Static, Continuous & PagedAttention: From early static batching waste to Orca iteration-level continuous scheduling and vLLM virtual memory paging.
Production SRE & Cluster Telemetry: Measuring TTFT, Inter-Token Latency (ITL) P99 SLOs, and memory fragmentation in mission-critical deployments.
Discrete-Event Simulation & Capacity Planning: Python simulator modeling queue delays, scheduling policies, and multi-tenant goodput.
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