GPU guides
Plain-English, in-depth explainers on GPU cloud pricing, hardware choices, and cost planning for AI workloads. All figures are illustrative examples for education — never quotes.
Start with the pricing models, estimate your GPU-hours, then use the decision guides to choose hardware for training, fine-tuning or serving. For a weekly read on what is moving in the GPU market, see the GPU Market Watch.
Inference vs Training: Why Serving a Model Usually Costs More Than Building It
Training is a one-off bill; inference is a bill that never stops. How to estimate both, the ratio most teams discover too late, and the levers that cut serving cost.
Read guide →GPU Utilisation: Why Your Bill Is Higher Than Your Compute
Most GPU bills pay for idle time. How to measure real utilisation, the six places it leaks, and a target range that is realistic for each kind of workload.
Read guide →Quantization for Cost Planning: FP16 to 4-Bit, What Changes and What It Costs
What each precision level means for memory, speed and quality, how to decide where on the ladder a workload belongs, and how to measure the quality cost instead of guessing.
Read guide →Spot GPU Instances: Checkpointing and Budgeting for Interruptions
Spot and preemptible GPUs are the biggest discount in cloud compute and the easiest way to lose a day of training. How to design jobs that survive interruption and how to budget for it honestly.
Read guide →Batch vs Real-Time Inference: Cost per 1,000 Requests, Explained
The same model can cost several times more per request served in real time than in batch. Why that is, how to calculate cost per thousand requests for each, and how to split a workload between the two.
Read guide →Reading a GPU Cloud Invoice: Every Line Beyond GPU-Hours
GPU-hours are often half the bill or less. Storage, egress, idle volumes, public IPs, snapshots and support plans make up the rest. How to read each line and which ones to cut.
Read guide →GPU Regions, Availability and Price: Why the Same Card Costs Different Amounts
The same GPU can be priced differently and be available differently across a provider’s regions. What drives the difference, how to plan around capacity, and when a farther region is the better deal.
Read guide →Choosing a GPU for Fine-Tuning: Memory Math for Full, LoRA and QLoRA Runs
Fine-tuning memory is weights plus gradients plus optimiser state plus activations. How each method changes that sum, which card each model size needs, and how to check your estimate before paying for a run.
Read guide →Choosing a GPU for Inference Serving: Latency, Throughput and Cost per Request
Serving is decided by three numbers: memory to hold the model, tokens per second per GPU, and the latency your users will accept. How to size a serving fleet and pick the cheapest card that meets the target.
Read guide →Copper vs. Optical Interconnect for AI Clusters: Reach, Power, and Cost Compared
Copper wins inside the rack, optics win everywhere else - and physics moves the line every generation. DAC, AEC, and fiber compared on reach, power, latency, and cost. Second in our optical interconnect series.
Read guide →800G and 1.6T Optical Modules Explained: Form Factors, Power, and When You Need Them
OSFP vs QSFP-DD, DR vs FR reach classes, and DSP vs LPO power - the module decisions that quietly set an AI fabric's cost and power bill. Third in our optical interconnect series.
Read guide →What Is Co-Packaged Optics (CPO)? Why AI Clusters Are Moving Light Onto the Chip Package
CPO puts the optical engine inside the switch package instead of a faceplate pluggable. The definition, the power math in plain numbers, and what it trades away. First in our optical interconnect series.
Read guide →Take-or-Pay GPU Contracts, Explained: What Axe Compute's $3.45B Backlog Means for Your AI Budget (2026)
Axe Compute signed $3.45B in GPU contracts buyers must pay whether they use the capacity or not. What take-or-pay means, who it fits, and five things to negotiate before you sign.
Read guide →Why GPU Cloud Prices Keep Rising in 2026 (and How to Stay Ahead of the Next Hike)
Nebius raised rates ~20% on October 1. Why the hikes keep coming — demand, Blackwell supply, power — and five levers to protect your AI budget.
Read guide →Run an LLM on Your Laptop in 2026: Memory, Runtimes, and the New Local-AI Playbook
What the October 7 RTX Spark moment means for local AI: unified-memory tiers, runtimes (Ollama, LM Studio, llama.cpp), and the hybrid router that decides what stays on your machine.
Read guide →How GPU Cloud Pricing Works: On-Demand vs Spot vs Reserved
The three pricing models every GPU cloud uses, when each one makes sense, and how billing actually works — with illustrative examples.
Read guide →A100 vs H100 vs L40S, Explained for Beginners
What separates NVIDIA's most popular data-center GPUs, which one fits training, fine-tuning, or inference — and which newer cards to know.
Read guide →GPU Cloud Provider Directory: Where to Check Real Prices
We don't publish live prices — so here's where to find them: official pricing pages for 15+ GPU clouds, organized by type.
Open directory →How to Estimate GPU Hours for Training and Fine-Tuning
A practical framework for turning model size, dataset size, and epochs into a GPU-hour budget, with a worked example.
Read guide →Multi-GPU Setups: NVLink, Scaling, and When You Need More Than One GPU
Data parallelism, NVLink vs PCIe, scaling efficiency, and how to tell when a single GPU is enough.
Read guide →How Much VRAM Do You Need to Run an LLM Locally? A Practical Sizing Guide (2026)
The VRAM equation, the quantization ladder, and sizing tables: what model size fits on 8GB to 80GB cards, and how context length changes the answer.
Read guide →Cost-Saving Strategies for Indie AI Developers
Spot instances, right-sizing, quantization, efficient fine-tuning, and other ways to stretch a small GPU budget.
Read guide →B200 (Blackwell) Cloud Pricing: What the Newest GPU Costs to Rent
The newest NVIDIA GPU, four clouds: Lambda, RunPod, Nebius, and CoreWeave B200 rates read from official pricing pages, plus the cluster tier and the H100 premium math.
Read guide →H100 Rental Prices Compared: What One H100 Hour Actually Costs Across Providers
One GPU, four clouds: RunPod, Lambda, Nebius, and CoreWeave H100 rates read from official pricing pages, plus what a full month actually costs.
Read guide →CoreWeave vs Lambda Labs: Pricing and Differences Explained
Two NVIDIA-partner GPU clouds, two different customers: pricing models, minimums, and how to choose between them.
Read guide →Cheapest Way to Run Stable Diffusion in the Cloud
Rental vs serverless for Stable Diffusion: the cheapest GPUs that work, per-image cost math, and free options.
Read guide →Cloud GPUs for Students and Researchers: Free and Cheap Options
Free tiers, education credits, cheap spot GPUs, and university clusters: affordable compute for coursework and research.
Read guide →Rent vs Buy a GPU for AI: A Practical Decision Framework
The true cost of owning vs renting, the crossover math with a worked example, and when each option wins.
Read guide →Glossary of GPU Terms: VRAM, TFLOPS, Tensor Cores, and More
Plain-English definitions of the jargon you'll meet when shopping for GPU compute.
Read guide →