Independent AI infrastructure research
AI compute choices, examined clearly.
Practical guides to Cloud API, cloud GPU, DIY local AI, and turnkey private AI for developers and businesses.
Current research
Choose the right AI compute path
Specification analysis and buyer guidance based on official sources—never invented benchmarks or testing claims.
NVIDIA DGX Spark: Who Is It Actually For?
A specification-led buyer's guide to DGX Spark, including the workloads it suits, the constraints buyers should verify, and the alternatives worth considering.
02 / ANALYSISDGX Spark vs Cloud GPUs: When Does Buying Hardware Make Sense?
DGX Spark vs cloud GPUs: a transparent framework for comparing cost, utilization, data locality, and operations without equating unlike accelerators.
03 / ANALYSISDGX Spark vs Mac Studio for Local AI Workloads
DGX Spark vs M5 Mac Studio for local AI: compare memory, CUDA/Core AI software fit, pricing, availability, and buyer trade-offs.
04 / ANALYSISNVIDIA DGX Spark vs Aradia Turnkey: What Are You Actually Paying For?
A neutral buyer's guide to bare NVIDIA DGX Spark versus Aradia's turnkey private AI deployment, including the price gap, engineering work, security configuration, support, and business fit.
05 / ANALYSISAradia Private AI Systems: DGX Spark vs DGX Station vs DGX B200
A current, neutral guide to Aradia's three published private AI appliance tiers, comparing price, compute class, memory, users, deployment scope, support, and when a bare or cloud alternative is better.
06 / ANALYSISCloud GPU Cost Calculator: Estimate Monthly AI Spend Before You Rent
A practical cloud GPU cost calculator framework for AI workloads, covering hourly compute, storage, bandwidth, utilization, idle time, and the local-versus-cloud break-even question.
07 / ANALYSISNVIDIA DGX B200 vs Aradia Turnkey DGX B200: Rack-Scale Cost and Deployment Guide
A neutral buyer's guide to bare NVIDIA DGX B200 procurement versus Aradia's turnkey rack-scale deployment, with price-comparison limits, engineering scope, facilities, SLA, and enterprise fit.
08 / ANALYSISNVIDIA DGX B200 vs DGX Station: Which AI System Fits the Workload?
A practical comparison of NVIDIA DGX B200 and DGX Station, covering memory, scale, power, networking, software, and the teams each system is designed to serve.
09 / ANALYSISNVIDIA DGX Spark vs ASUS Ascent GX10: GB10 Specs and Buyer Trade-offs
DGX Spark and ASUS Ascent GX10 share the GB10 platform. Compare their memory, storage, networking, OS, software fit, and purchasing risks before choosing a local AI system.
10 / ANALYSISNVIDIA DGX Spark vs Dell Pro Max with GB10: Price, Support, and Fit
Compare DGX Spark with Dell Pro Max with GB10 on current listed configuration, memory, storage, networking, support, software, and local AI workload fit.
11 / ANALYSISDGX Spark vs DGX Station vs Cloud GPUs: Which AI Setup Fits Your Team?
A practical comparison of DGX Spark, DGX Station, and cloud GPUs, covering memory, software, power, concurrency, cost uncertainty, and the right proof-of-concept path.
12 / ANALYSISDGX Spark vs HP ZGX Nano vs Acer Veriton GN100: GB10 Mini Workstation Guide
Compare NVIDIA DGX Spark, HP ZGX Nano, and Acer Veriton GN100 across memory, storage, networking, software, support, and local AI deployment fit.
13 / ANALYSISNVIDIA DGX Spark vs Lenovo ThinkStation PGX: Which GB10 System Fits?
Compare NVIDIA DGX Spark and Lenovo ThinkStation PGX on GB10 architecture, memory, software, connectivity, availability, procurement, and workload fit.
14 / ANALYSISNVIDIA DGX Station vs Aradia Turnkey DGX Station: What Does the Premium Buy?
A neutral comparison of NVIDIA DGX Station hardware and Aradia's turnkey deployment, including pricing limits, memory, staffing, security configuration, support, and who should skip the premium.
15 / ANALYSISHow Much GPU Memory Do You Need for Local LLMs? A Practical VRAM Guide
A practical VRAM planning guide for local LLM inference, covering parameter counts, quantization, context overhead, and when system RAM or cloud GPUs make more sense.
16 / ANALYSISLocal LLM Hardware FAQ: VRAM, Quantization, Context, and Cloud GPUs
Answers to the practical questions that determine whether a local LLM setup will fit, run fast enough, and remain manageable as models and users grow.
17 / ANALYSISLocal LLM Hardware Buying Checklist: 10 Questions to Answer Before You Buy
A workload-first checklist for choosing local LLM hardware, covering memory, quantization, software support, thermals, storage, privacy, and when to rent a cloud GPU instead.
18 / ANALYSISNVIDIA DGX Station: Who Is It Actually For?
A specification-led guide to NVIDIA DGX Station, its large coherent memory design, multi-user features, and the teams that should choose it over DGX Spark, a conventional workstation, or a data-center system.
19 / ANALYSISWhy Enterprise AI Hardware Costs So Much: NVIDIA DGX B200 Explained
A buyer-focused explanation of the cost drivers behind NVIDIA DGX B200, from HBM3e and NVLink to power, networking, support, and data-center operations.
How we work
Research built for decisions, not pageviews.
- Primary sources before summaries
Manufacturer documentation and official pricing pages anchor material claims.
- Estimates remain estimates
Cost models expose their inputs and avoid presenting arithmetic as measured performance.
- Fit over universal rankings
Hardware and cloud recommendations are tied to workload, team, and operational constraints.