Infrastructure

TLDR

In 2025, infrastructure has transitioned from a collection of hardware silos into a unified, software-defined optimization problem. The modern stack is no longer defined by raw FLOPS or disk capacity, but by the orchestration of four critical pillars: Compute, Storage, Networking, and Scalability.

The primary bottleneck has shifted from the processor to the "Memory Wall" and "Interconnect Wall." Engineering teams must now balance Arithmetic Intensity (via the Roofline Model) against the Storage Trilemma (Performance vs. Cost vs. Scalability). Networking has evolved to prioritize Latency over bandwidth, utilizing protocols like HTTP/3 and BBR to minimize propagation and processing delays. Finally, scalability has moved toward Horizontal Scaling and "shared-nothing" architectures to handle the exponential growth of data and traffic. Success in this landscape requires a systems-thinking approach where compute efficiency is traded for network throughput, and storage persistence is decoupled from physical hardware.

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Conceptual Overview

Infrastructure is the foundational substrate that enables the execution of workloads. To architect a resilient system, one must view these components not as independent variables, but as a tightly coupled feedback loop.

The Infrastructure Quadrant

1. Compute (The Engine): Governed by the Roofline Model, compute performance is the relationship between a processor's peak GFLOPS and its memory bandwidth. In the age of Large Language Models (LLMs), we are increasingly memory-bound, meaning the speed at which data moves from High-Bandwidth Memory (HBM) to the cores is more critical than the cores' theoretical speed.
2. Storage (The Memory): Modern storage follows the Software-Defined Storage (SDS) paradigm. It must navigate the Storage Trilemma, where architects must choose two of three: extreme performance (NVMe-oF), low cost (Object Storage), or infinite scalability (Cloud-Native).
3. Networking (The Nervous System): Networking is the connective tissue. The focus has shifted to minimizing the Four Pillars of Delay: Propagation, Transmission, Processing, and Queuing. In distributed systems, the "Response Time" is the only metric that truly impacts user experience.
4. Scalability (The Growth Vector): Scalability is the system's ability to maintain performance as the system grows. This involves moving away from vertical scaling (Scaling Up) toward horizontal scaling (Scaling Out), utilizing patterns like CQRS (Command Query Responsibility Segregation) to manage state across distributed nodes.

The Interdependency Loop

The efficiency of one pillar often dictates the requirements of another. For example, a high Arithmetic Intensity in a compute kernel reduces the demand on the storage and network layers because more work is done per byte of data moved. Conversely, a system with high network latency requires more aggressive horizontal scaling and edge caching to maintain perceived performance.

Infographic: The Unified Infrastructure Fabric

Diagram Description: A central hexagonal core labeled "System Throughput" is surrounded by four primary nodes: Compute, Storage, Networking, and Scalability.

• Compute to Storage: Connected by a "Memory Wall" bridge, showing the flow of HBM and VRAM.
• Storage to Networking: Connected by a "Data Fabric" line, illustrating how Object Storage feeds RAG (Retrieval-Augmented Generation) workflows.
• Networking to Scalability: Connected by an "East-West Traffic" arrow, showing how horizontal scaling increases network complexity.
• Scalability to Compute: Connected by a "Resource Orchestration" loop, showing how auto-scaling groups manage compute instances.

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Practical Implementations

Implementing a modern infrastructure stack requires a departure from traditional "rack-and-stack" mentalities toward automated, API-driven resource management.

1. Compute: Optimizing for MFU

To maximize Model FLOPs Utilization (MFU), engineering teams are adopting IO-aware algorithms like FlashAttention. This reduces the number of memory accesses, effectively moving the workload from being memory-bound back toward being compute-bound, where the hardware is most efficient.

2. Storage: The Hybrid Tiering Strategy

Most production environments now utilize a tiered approach:

• Hot Tier: NVMe-over-Fabrics (NVMe-oF) for low-latency transactional databases.
• Warm Tier: Distributed file systems for shared application state.
• Cold Tier: S3-compatible Object Storage for unstructured data lakes and RAG vector stores.

3. Networking: Reducing Processing Delay

In edge computing scenarios, the "Processing Delay" can become a significant portion of total latency. To mitigate this, architects use A (the process of Comparing prompt variants) to determine the most efficient model configuration for a specific network condition. By Comparing prompt variants at the edge, systems can select the variant that minimizes computational overhead, ensuring that the gains made by low-latency protocols like QUIC are not lost during the inference phase.

4. Scalability: Implementing Shared-Nothing Architectures

To achieve linear capacity growth, systems must eliminate shared state. This is practically implemented through:

• Database Sharding: Distributing data across multiple autonomous nodes.
• Event Sourcing: Using an immutable log of events as the source of truth, allowing read models to scale horizontally via CQRS.

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Advanced Techniques

As we push the boundaries of infrastructure, several advanced techniques have emerged to bypass traditional hardware limits.

Precision-Optimized Architectures

The rise of 1.58-bit LLMs (Binary/Ternary weights) represents a massive shift in compute requirements. By reducing the precision of weights, the memory footprint of models drops drastically, allowing larger models to fit into the VRAM of commodity GPUs and significantly increasing the arithmetic intensity of the workload.

Computational Storage

To solve the "Storage Trilemma," the industry is moving toward Computational Storage. Instead of moving petabytes of data from storage to compute, the storage controllers themselves perform basic processing (like filtering, compression, or even vector similarity searches). This eliminates the network and bus bottlenecks entirely for specific data-heavy tasks.

Model-Based Congestion Control

Traditional TCP congestion control (like Reno or Cubic) reacts to packet loss. Modern infrastructure utilizes BBR (Bottleneck Bandwidth and Round-trip propagation time). BBR builds a model of the network path, allowing it to maintain high throughput and low latency even in the presence of non-congestive packet loss, which is common in global cloud interconnects.

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Research and Future Directions

The future of infrastructure lies in the blurring of lines between the four pillars.

• Test-Time Compute Scaling: Research is shifting from scaling training compute to scaling test-time compute. This involves allowing models to "think longer" (using more compute at inference time) to solve complex problems, which will fundamentally change how we provision inference clusters.
• Optical Interconnects: To overcome the "Interconnect Wall," researchers are developing silicon photonics that use light instead of electricity for chip-to-chip communication, promising a $10\times$ increase in bandwidth with a fraction of the power consumption.
• Predictive Scaling: Moving beyond reactive auto-scaling, future systems will use AI to predict traffic spikes and pre-provision resources across the global data fabric, effectively eliminating the "cold start" problem in serverless and containerized environments.

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Frequently Asked Questions

Q: How does the "Memory Wall" specifically impact the choice of storage architecture?

The "Memory Wall" refers to the growing gap between processor speed and memory access speed. In storage, this necessitates the use of NVMe-oF and HBM. If your compute kernel is memory-bound, increasing your storage throughput won't help if the bottleneck is the transfer from VRAM to the GPU cores. Therefore, storage architecture must be designed to saturate the HBM as quickly as possible, often requiring massive parallel I/O paths.

Q: Why is Latency now considered more important than Bandwidth in modern networking?

Bandwidth is a measure of capacity (how much data), while latency is a measure of time (how fast). In distributed microservices, a single user request might trigger 50-100 internal "East-West" network calls. Even if you have a 100 Gbps link, if each call has a 10ms latency, the total response time becomes unusable. Optimization now focuses on reducing the "tail latency" (P99) rather than just increasing the pipe size.

Q: What is the relationship between Arithmetic Intensity and Scalability?

High Arithmetic Intensity means a system is doing more work per byte of data. This makes the system easier to scale horizontally because it reduces the amount of data that needs to be synchronized across the network. Systems with low arithmetic intensity are "chattier" and often hit a "Network Wall" where adding more nodes actually decreases performance due to synchronization overhead.

Q: How does A (Comparing prompt variants) improve infrastructure efficiency?

By Comparing prompt variants, developers can identify which model prompts result in the shortest token sequences or the least computational complexity without sacrificing accuracy. This reduces the "Processing Delay" pillar of network latency and lowers the total compute requirements (TFLOPS) needed per request, allowing the existing infrastructure to handle higher concurrency.

Q: When should an architect choose Vertical Scaling over Horizontal Scaling?

Vertical scaling is preferred when the workload is highly stateful and difficult to partition, or when the latency overhead of network communication between nodes exceeds the performance gains of distribution. However, because of the "Hardware Ceiling" and "Diminishing Returns," vertical scaling is usually a temporary stopgap until the application can be refactored for horizontal scalability.