Deep dives from our engineering team
Practical guides on React, Node.js, DevOps, AI, and building production software. Written by developers who ship daily.
- Articles
- 607+
- Readers/mo
- 500+
- Topics
- 25
Found 37 articles in Hardware
Hardware11 min read
Optimize AI Training Hardware Scaling for Peak Performance
Scaling AI model training effectively requires a deep understanding of hardware interplay, from the latest GPUs to high-bandwidth interconnects and robust server infrastructure. This guide cuts through the noise, translating complex specs into actionable strategies for achieving peak performance in your datacenter.
Hardware10 min read
Choosing Low-Latency LLM Inference Hardware for Real-Time AI
Achieving real-time responses from large language models demands specialized hardware. We dive into the critical factors—from memory bandwidth to dedicated accelerators—that dictate performance for low-latency LLM inference, helping engineers and founders make informed decisions for their AI infrastructure.
Hardware10 min read
NPU Hardware Selection: Powering Efficient On-Device AI
The rise of on-device AI demands specialized hardware. Navigating the landscape of NPUs, from mobile chipsets to dedicated accelerators, requires a deep understanding of power envelopes, latency, and model constraints to deliver performant, cost-effective solutions.
Hardware10 min read
Beyond GPUs: Mastering Memory & Storage as the Real AI Hardware Bottleneck
While GPUs grab headlines, the silent saboteurs of AI performance are often memory and storage. Understanding the hierarchy from HBM to NVMe is critical for preventing bottlenecks and maximizing throughput in complex AI and developer workloads.
Hardware10 min read
Decoding AI Hardware Specs: A Practical Guide for Engineers
Navigating the complex world of AI hardware specifications can be daunting. This guide empowers engineers and ML practitioners to move beyond marketing hype, offering practical insights into what key metrics like VRAM bandwidth, Tensor Core throughput, and PCIe generation truly mean for your AI development and deployment.
Hardware11 min read
Build Your Local LLM Fine-Tuning Hardware: A Developer's Guide
Fine-tuning large language models (LLMs) locally demands specific hardware considerations beyond just inference. This guide from Krapton Engineering details the critical components—GPU VRAM, memory bandwidth, CPU, and storage—and provides practical recommendations to optimize your developer workstation for efficient, cost-effective LLM fine-tuning.
Hardware11 min read
InfiniBand vs Ethernet for AI: Optimizing Network Interconnects for Training
Choosing the right network interconnect is critical for scalable AI training. We break down InfiniBand and high-speed Ethernet, comparing their performance, cost, and suitability for distributed deep learning workloads, ensuring your GPUs aren't bottlenecked.
Hardware9 min read
Developer Workstation Hardware: Apple Silicon vs. x86 for Peak Performance
The right developer workstation hardware dictates productivity and project velocity. As architectures evolve, choosing between Apple Silicon and x86 requires a deep dive into real-world performance for diverse engineering tasks, from complex compilations to local AI model inference.
Hardware10 min read
Mastering Apple Silicon for AI Development & Inference
Apple Silicon processors, with their unified memory architecture and integrated Neural Engines, are transforming local AI development and inference. Discover how M-series chips deliver surprising performance for machine learning workflows, balancing power efficiency with raw computational ability for engineers and founders building AI applications.
Hardware11 min read
Choose the Right AI Accelerators for Inference & Fine-tuning
The choice of AI accelerators profoundly impacts the performance and cost-efficiency of LLM inference and fine-tuning. This guide cuts through the marketing hype to provide engineers and founders with practical insights into selecting the right hardware for enterprise-grade AI workloads.
Hardware10 min read
Optimizing Your Developer GPU for AI Workflows
Choosing the right GPU for local AI development is critical for productivity and cost efficiency. We break down the crucial specs like VRAM and memory bandwidth, comparing NVIDIA, AMD, and Apple Silicon for real-world machine learning tasks.
Hardware12 min read
NVMe Storage for Web Applications: Unlock Peak Performance
In 2026, slow I/O is a silent killer for web applications and AI workloads. Traditional storage bottlenecks can cripple user experience and data processing. Explore how NVMe storage, from local servers to cloud instances, delivers the speed and responsiveness modern applications demand.
Hardware11 min read
Enterprise AI Accelerators: Powering Scalable Training & Inference
Selecting the right enterprise AI accelerators is critical for driving performance and managing costs in modern AI workloads. From model training to large-scale LLM inference, understanding the nuances of datacenter GPUs and specialized hardware is paramount for scalable and efficient AI infrastructure.
Hardware10 min read
Architecting High-Performance AI Infrastructure for Enterprise Scale
Building AI applications that truly scale requires more than just powerful GPUs; it demands a thoughtfully architected high-performance AI infrastructure. From large-scale model training to real-time inference, selecting the right compute, networking, and storage components is critical for achieving efficiency and controlling costs.
Hardware10 min read
Crafting Your Enterprise AI Hardware Strategy: Cloud vs. On-Prem
Choosing the right hardware for enterprise AI is a critical strategic decision, impacting performance, cost, and long-term scalability. This guide explores the trade-offs between cloud-based and on-premise AI infrastructure, offering practical insights for engineering leaders.
Hardware11 min read
Choosing LLM Inference Hardware: VRAM, Bandwidth & Cost Efficiency
Selecting the optimal LLM inference hardware is a critical decision impacting performance and cost. This guide breaks down the essential factors like VRAM, memory bandwidth, and quantization, offering practical insights for on-premise and cloud deployments.
Hardware11 min read
Powering Local LLM Inference: Best Hardware for Developer Workstations
As AI development accelerates, running Large Language Models locally on developer workstations is becoming critical for rapid iteration, privacy, and cost control. This guide dives into the essential hardware choices, from high-VRAM GPUs to Apple Silicon, helping you pick the right setup for your local LLM inference needs.
Hardware7 min read
ARM for Cloud Computing: A Strategic Shift for Performance & Cost
ARM processors are redefining cloud infrastructure for 2026, offering superior price/performance and energy efficiency over traditional x86 architectures. Discover how Graviton and Ampere drive cost savings and boost AI inference for modern applications.
Hardware13 min read
Self-Hosting LLM Inference: Architecting On-Premise AI Hardware
The decision to self-host LLM inference on-premise demands a rigorous evaluation of hardware architectures. Beyond just GPUs, the entire system — from VRAM to networking and cooling — dictates performance, cost-efficiency, and scalability for real-world AI applications. This guide provides a principal engineer's perspective on building robust, cost-effective inference infrastructure.
Hardware9 min read
Apple Silicon for Developers: Performance, Efficiency, and AI Power
Choosing the right developer workstation is a critical decision impacting productivity, build times, and local AI capabilities. This deep dive compares Apple Silicon (M-series) with x86 architectures, assessing their real-world performance, energy efficiency, and suitability for diverse development workflows, including heavy AI/ML tasks.
Get engineering insights delivered to your inbox
One deep-dive every week. No spam. Unsubscribe anytime.
Join engineers who get our articles first.