QuantaCloud field notes
GPU infrastructure, explained.
Practical analysis of hardware, pricing, capacity planning, and production infrastructure.
What Makes a GPU Data Center Different from Traditional Infrastructure
A gpu data center is fundamentally different from traditional colocation. Here is what sets GPU-optimized facilities apart and what to look for in a partner.
The Real Cost of Cloud GPU Server Downtime During a Training Run
Concrete math on what it costs when a cloud GPU server fails mid-training, and why infrastructure reliability matters more than price per hour.
Reserved vs. On-Demand GPU Rental: When Each Makes Sense
A practical guide to choosing the right GPU rental model for your AI workloads, comparing reserved and on-demand compute.
GPU Benchmark Comparison: What the Numbers Actually Tell You
A practical gpu benchmark comparison that cuts through synthetic scores to explain what actually matters for training and inference performance.
A100 vs H100: A Practical Guide for AI Workloads
A practical comparison of the a100 vs h100 for training and inference workloads, covering specs, real performance gaps, pricing, and when each GPU is the right choice.
InfiniBand vs Ethernet: Choosing the Right Interconnect for Your GPU Cluster
A technical comparison of InfiniBand and Ethernet for GPU cluster networking, with real latency numbers and guidance on when each interconnect is worth the investment.
Why Every AI Team Needs a Multi-Provider GPU Cloud Provider Strategy
Relying on a single gpu cloud provider creates real risk. Here is how to build resilient GPU infrastructure across multiple partners and avoid capacity bottlenecks.
H100 vs H200: Is the Upgrade Worth the Premium
A practical comparison of h100 vs h200 covering memory, bandwidth, inference performance, and whether the pricing premium justifies the upgrade for your workloads.
How to Evaluate a GPU Server Hosting Provider
The questions most teams forget to ask when choosing a gpu server hosting provider, from failover policies and oversubscription to what managed really means.
High Performance Computing GPU Infrastructure for AI Teams
What high performance computing gpu infrastructure actually requires for modern AI workloads, from InfiniBand networking and NCCL tuning to parallel file systems and cluster design.
H100 Pricing vs B300: When Migration Makes Financial Sense
A practical look at H100 pricing versus B300 costs, covering memory differences, workload fit, and when the migration premium pays for itself.
GPU for Deep Learning: Choosing the Right Hardware in 2026
Choosing the right gpu for deep learning depends on model size, training precision, and inference volume. Here is how to match hardware to workload in 2026.
Why Your Dedicated GPU Server Takes Longer Than Expected to Deploy
The hidden bottlenecks that delay dedicated GPU server procurement and how to eliminate each one before it stalls your deployment timeline.
GPU for Machine Learning: A Practical Hardware Guide
The right gpu for machine learning depends on your workload. This guide covers hardware choices for classical ML, deep learning, and cost-effective GPU options.
Why Bare Metal GPU Clusters Rarely Beat Managed Infrastructure
Total cost of ownership analysis for bare metal GPU deployments. When managed infrastructure pays for itself versus building your own gpu cluster.
GPU Server Rack Design: What to Know Before You Deploy
The gpu server rack is the fundamental unit of modern AI infrastructure. Here is what teams need to understand about power, cooling, networking, and density before deploying their first rack.
How to Rent GPU for AI at Every Startup Stage: Series A Through C Capacity Planning
A stage-by-stage guide to how AI startups rent GPU for AI workloads as they scale from 2 GPUs at Series A to 128 or more at Series C.
The HGX H100: Why It Matters for Multi-Node Training
The hgx h100 baseboard is the building block of large-scale GPU training clusters. Here is what it is, how it connects across nodes, and what to ask your provider.
Finding the Cheapest GPU Cloud Does Not Help When Your Provider Sells Out
Teams that optimize only for the cheapest gpu cloud often end up locked into a single provider with no failover. Here is what happens when that provider runs out of capacity.
AI GPU Server Requirements: What to Spec for Training and Inference
How to spec an ai gpu server for training, inference, and fine-tuning workloads, covering CPU, memory, storage, networking, and the mistakes teams make when sizing hardware.
AI Compute Infrastructure: The Hidden Cost of Doing It Yourself
The full cost stack of building your own ai compute infrastructure is larger than most teams expect. Here is what gets missed and when managed alternatives pay for themselves.
Multi GPU Training: How to Scale Without Wasting Compute
A practical guide to multi gpu training that covers parallelism strategies, communication overhead, and how to maximize GPU utilization as you scale from 8 to 512 GPUs.
GPU for Inference: Sizing Your Hardware for Production Workloads
A practical guide to choosing the right gpu for inference in production, covering VRAM constraints, batching strategies, and why cost per inference matters more than peak throughput.
GPU Colocation vs Cloud: Which Makes Sense for Your Team
Comparing gpu colocation to cloud GPU for AI workloads. Cost analysis, operational tradeoffs, and when each model works best over 12 to 36 months.