GPU cloud knowledge base

DigitalOcean GPU Droplet pricing per GPU-hour, checked against live rates from nine providers, plus the 5 TiB scratch disk that does not survive a destroy.

The best GPU for LLM inference depends on your segment. H100 for production serving, A100 for training value, L40S for local dev, with real specs and pricing.

On-demand H100, A100, L40S, and RTX 4090 rates from 8 providers' own pricing pages, plus the hidden costs that decide your real bill. Dated August 2026.

H100 rental rates bottomed near $2.79/GPU-hr in mid-2025 and climbed past $2.82 by April 2026. The supply data behind the reversal.

CoreWeave competitors and Lambda alternatives with real August 2026 pricing across neoclouds, marketplaces and hyperscalers, plus honest commitment terms.

Every real free GPU option for 2026: always-free tiers like Colab and Kaggle, plus application-based programs worth $2K to $350K, with catches named.

Honest comparison of 8 Google Colab alternatives: Kaggle, RunPod, Vast.ai, Lambda, Modal, SageMaker Studio Lab and more, with real prices and free tiers.

GPU as a service means renting GPU compute instead of buying it. The five delivery models, real August 2026 hourly rates, and which model fits which workload.

The actual arithmetic for LLM VRAM sizing (weights, KV cache, and overhead), plus a model x quantization x GPU table so you stop guessing at capacity.

NVIDIA H100 VRAM by variant: 80GB HBM3 on SXM5, 80GB HBM2e on PCIe, 94GB per GPU on NVL. Full verified spec table and how to size a model against it.

What NVIDIA Inception gives you, who qualifies, how to apply, and an honest read on the cloud credits and hardware pricing, verified against NVIDIA's own FAQ.

PyTorch ships official ROCm wheels, but flash-attention, xformers and bitsandbytes stay experimental on AMD. The real picture, August 2026.

Nine RunPod alternatives compared on real hourly GPU pricing, strengths, and best use case: from Vast.ai's bid market to CoreWeave's dedicated capacity.

Serverless GPU pricing beats a dedicated box below roughly 69% utilization, above it dedicated wins. Real provider prices and the worked break-even math.

TPU vs GPU compared on architecture, current-gen specs, software, and price: Google's Ironwood TPU against NVIDIA's H100/H200, with a clear decision framework.

A neocloud is a cloud provider built only for GPU compute. What that means, why they exist, who the notable ones are, and what you give up choosing one.

How to install ComfyUI Manager, fix the missing custom nodes in a workflow, pin a working set with snapshots, and get past the errors that block installs.

Scripting a ComfyUI node and model install with comfy-cli: exact commands, the flag that catches dependency conflicts, and what still won't survive.

The 8 ComfyUI custom node packs almost everyone installs: exact registry IDs, real dependency traps from each repo's own issues, and what breaks.

Running ComfyUI on AMD GPUs with ROCm: which PyTorch build to use, which custom nodes break, and what an MI300X actually does on image and video workflows.

There's no single MI300X-vs-H100 multiplier. Real vLLM, MLPerf, and SemiAnalysis numbers, plus the software gaps that decide the outcome.

Lambda Cloud has no stop button, only launch, restart, or terminate. What that means for your disk, and what a persistent filesystem buys you.

A powered-off Paperspace machine still bills storage, IP and add-ons hourly. What actually stops the meter, and what block storage costs per GB.

Vast.ai bills disk while an instance is stopped, and a Volume can't leave its host. What actually survives destroy, and what doesn't.

What a GPU rental marketplace actually is, the four structural types, and the six things to check before you rent, with live numbers from 744 offers.

Provider snapshots are region-locked and volume-bound by design. What a portable GPU snapshot must capture, and the driver trap that breaks the restore.

Zero percent GPU utilization is also what thinking looks like. How a correct automatic idle stop decides, and why it must refuse to guess.

We pulled every offer from our marketplace feed across 8 providers. Picking the right provider saves less than you think; the right host saves more.

The MI300X has 192GB of HBM3, more than an H100 or H200, from $1.71 to $12/hr. Verified prices, the sparsity trap, and an honest read on ROCm.

Across 80 live offers the same RTX 5090 ranges from $0.158 to $2.004 per hour. What drives the spread, and what 32GB and FP4 buy you over a 4090.

RunPod has three storage types and they are not interchangeable. What container disk, volume disk and network volume each keep when a pod stops or terminates.

ComfyUI custom nodes reinstall every session on a fresh cloud GPU. Why it happens, what ComfyUI-Manager's snapshot really saves, and what actually fixes it.

Five real ways to stop paying for a cloud GPU when idle, each mechanism, and the specific catch that keeps your meter running.

Terminate a cloud GPU box and the disk is deleted, with no backup to restore from. Why it happens, why a volume is not a backup, and how to stop losing work.

Moving a GPU workload to another cloud provider is easy to start and hard to finish. What actually has to come with the box, and how to move it clean.

Idle time is 30 to 95 percent of most cloud GPU bills. Here is the actual per-hour and per-month math by GPU class, and where the waste hides.

A checkpoint saves your model, not the machine that trained it. The difference between saving a model and saving the whole training environment, mid-run.

When your provider runs out of capacity and you rent elsewhere, a fresh box makes you re-resolve torch, CUDA and every wheel. requirements.txt won't save you.

A GPU volume is region-locked and bills while idle, which is what keeps you stuck on one provider. Why storage cannot follow you to a cheaper one.

A volume snapshot brings back your files, not your working box: venv, custom nodes, models, OS state. What actually restores your setup.

A reclaimed spot GPU wipes the whole environment, not just data. Why checkpoint-and-restart falls short, and why recovery must be set up before the reclaim.

A preempted spot GPU can vanish with seconds of notice. Why volumes and disk snapshots only bring back data, and what has to capture your box beforehand.

Stopping a pod does not stop the bill, and suspend expires. What idle GPU time really costs, and how to pause a box and resume the exact setup anywhere.

Spot GPUs are cheap until a reclaim wipes your run. Why checkpoint scripts alone fail, and what has to already be capturing your box to survive an interruption.

Committing to one GPU provider means rebuilding everything the day you leave. How to build an environment not locked to one provider's region.

A RunPod network volume bills while idle and cannot leave its datacenter. Six ways to migrate off one, ranked by cost and portability.

How to run ComfyUI online without rebuilding it each time: the same nodes, models and workflows on any cloud GPU you rent, across providers.

Where the ComfyUI models folder lives, why cloud GPUs lose it on every restart, and how to keep checkpoints and LoRAs off the download path for good.

Running ComfyUI in the cloud means rebuilding custom nodes and re-downloading models on every new GPU. How to keep a ComfyUI cloud setup that survives the swap.

Six RunComfy alternatives for running ComfyUI on cloud GPUs, priced and compared: managed clouds, raw GPU pods, buying your own card, and portable studios.

A real training run moved across three GPU boxes: process data on an A100, snapshot, resume training on a 5090, then pick it up again on another VM.

A step-by-step walkthrough of running FLUX diffusion inference faster on a multi-GPU cloud VM with xfuser (xDiT): CUDA setup, install, and parallel inference.

Vertex AI, SageMaker, Azure Foundry, Hugging Face and the GPU-native clouds compared on supply, governance and cost, and which one fits your workload.

Idle compute, not hardware, drives your GPU bill. Make training interruptible, checkpoint properly, and migrate when prices shift: the levers behind a 40% cut.

A100 vs H100 vs H200 on VRAM, throughput and cost per completed run: which one actually fits your training or inference job, and when upgrading pays off.

Shadeform shows you which GPUs are available; Aquanode runs and moves the workload. Where the two differ on deployment, monitoring and workload mobility.

H100 rates swing from under $2 to over $8 an hour. Three practices that keep you at the low end: cross-provider search, idle-free training, checkpointing.

Real hourly rates for H100, A100, 4090 and more on Aquanode vs RunPod, plus the throughput and cold-start costs a price-per-hour table always hides.