64 stories in Hardware Buying Guides — nothing ever gets deleted.
Manage multiple training rigs, inference servers, and dev workstations from one desk—or from anywhere. Real hardware KVM switches, IP KVM appliances, and software remote tools ranked by use case and budget.
Diego RamosThe GPU gets the glory, but the host CPU determines whether your multi-GPU AI rig hits its ceiling or its potential — here's how AMD Threadripper PRO, Intel Xeon W, and AMD EPYC stack up for real ML workloads in 2026.
Kaito TanakaA practical US buying guide to Ryzen AI Max laptops for local LLM inference, covering memory capacity, measured performance, Apple Silicon trade-offs, verified prices, and models that actually exist.
Diego RamosAll three cards carry 16GB, but their memory bandwidth, software stacks and July 2026 street prices make them materially different AI purchases. Here is where the RTX 5070 Ti, RTX 5080 and Radeon RX 9070 XT fit local LLMs, image generation and development work.
Kaito TanakaA practical, value-first guide to the Radeon RX 9060 XT, RX 9070, RX 9070 XT and RX 9070 GRE for local inference, PyTorch, LoRA and ROCm workloads, current as of July 27, 2026.
Diego RamosA methodical comparison of Thunderbolt 4 and Thunderbolt 5 docks for laptop-primary AI/ML workstations, emphasizing shared bandwidth, fast storage, displays, networking, charging, and honest availability.
Kaito TanakaEdge AI hardware is easiest to buy when you start with the workload, not the biggest TOPS number on the box — here is the value-first, spec-grounded breakdown of every ARM single-board computer worth buying for local inference in mid-2026.
Diego RamosSustained AI and ML workloads expose cooling weaknesses that short benchmarks can miss. These are the CPU coolers we would trust for mainstream AM5 and LGA workstations, quiet 24/7 operation, and full-coverage Threadripper cooling.
Kaito TanakaThe best local-AI mini PC is not necessarily the one with the biggest TOPS number. This guide focuses on usable memory, bandwidth, model fit, software support, and what you actually get for your money.
Diego RamosFive evidence-backed PC cases for sustained AI and machine-learning workloads, compared on high-TDP GPU fit, airflow, cable routing, storage, maintenance and realistic multi-GPU feasibility.
Kaito TanakaWith street prices now dipping below $200 and Zen 5 IPC gains finally landing in mid-range territory, the Ryzen 5 9600X is rewriting the value equation for student and creator rigs alike.
Diego RamosNVIDIA's RTX 5090 lands with 32 GB GDDR7 and 1.79 TB/s of memory bandwidth. We run the numbers on what that actually means for running 70B-class models locally in 2025.
Kaito TanakaBuilding a capable AI machine doesn't require a cloud-sized budget — with the right component priorities and smart refurbished GPU picks, you can assemble a 16GB VRAM powerhouse for under $1,500 in 2026. Here's the complete, value-first guide to two full builds that will let you run LLMs, generate images, and fine-tune models without breaking the bank.
Diego RamosA comprehensive hardware buying guide for AI/ML developers and data scientists, breaking down the best 4K, ultrawide, and OLED monitors by budget and use-case for peak productivity and eye comfort in 2026.
Kaito TanakaA practical, value-first guide to choosing an ATX 3.1 power supply for single-GPU AI/ML workstations in 2026 — how to size wattage, when Gold beats Titanium, and which units are smart money versus false economy.
Diego RamosA power outage mid-training run is not just an inconvenience — it can corrupt checkpoints, damage hardware, and cost days of compute. Here is the spec-grounded, tier-by-tier guide to choosing the right UPS for your AI/ML workstation in 2026.
Kaito TanakaA practical, price-to-performance guide to NVMe SSDs for local AI/ML work in 2026 — why storage speed matters for dataset loading and checkpointing, and which drives are worth your money across budget, mid-range, and high-end tiers.
Diego RamosThe interconnect between your GPUs is as critical as the GPUs themselves — choose wrong and expensive accelerators sit idle waiting on the network. Here is the spec-grounded, tier-by-tier breakdown of switches, NICs, and cabling for home-lab and small-team multi-node AI training setups in 2026.
Kaito Tanaka