DeployCue Cloud Cost Blog
Practical guides for developers and ML teams: how to choose a GPU host, cutting egress costs, LLM API pricing, spot vs on-demand, storage tiers, Kubernetes economics, and cloud billing explained.
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Fine-Tune Llama With LoRA on a Single Cloud GPU
Fine-tune a Llama model with LoRA on one rented cloud GPU, keeping memory low and costs predictable while still shipping a custom model.
Set Up Docker With GPU Passthrough for Reproducible ML Environments
Configure Docker GPU passthrough so your ML environment runs the same on any cloud GPU instance, from your laptop to a rented H100.
Mount Object Storage to a GPU Instance for Training Data
Stream training data from object storage to your GPU instance without filling local disk, using FUSE mounts and smart caching.
Measure Tokens Per Second on Your GPU: A Benchmarking Tutorial
Learn to benchmark tokens per second on any cloud GPU so you can compare inference speed honestly before you commit to an instance.
Run a GPU Workload on Kubernetes: From Node Pool to Pod
A practical tutorial to run a GPU workload on Kubernetes, from creating a GPU node pool to scheduling a pod that uses the accelerator.
Connect Jupyter to a Remote Cloud GPU in 10 Minutes
Get a Jupyter notebook running on a remote cloud GPU fast, with a secure connection and your local browser as the interface.
Set Up a Fault-Tolerant Spot Training Job From Scratch
Build a training job that survives spot interruptions through checkpointing, automatic resume, and a sensible fallback.
Deploy an LLM With vLLM on a Cloud GPU: Full Walkthrough
A complete walkthrough to serve an open LLM with vLLM on a rented cloud GPU, from install to an OpenAI-compatible endpoint.
Rent Your First Cloud GPU on RunPod: A Step-by-Step Tutorial
A beginner-friendly walkthrough to rent, connect to, and safely shut down your first cloud GPU on RunPod.
The ML Infrastructure Cost Optimization Checklist for 2026
A practical, ordered checklist to cut machine learning infrastructure costs across compute, storage, networking, and scheduling.
Auditing Shadow GPU Spend: Finding Forgotten Instances
Hunt down orphaned GPU instances, idle reservations, and untagged spend that quietly drains your cloud budget every month.
Kubernetes GPU Bin-Packing: Squeezing More Jobs onto Fewer Nodes
Tighten GPU scheduling on Kubernetes with bin-packing, sharing, and the right requests so fewer nodes do more work.
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Deploying Mixtral and MoE Models: Cost Quirks of Sparse Experts
Mixture-of-experts models like Mixtral are cheap to run but expensive to hold in memory. That quirk drives every cost decision.
Inference Autoscaling: Handling Traffic Spikes Without Overpaying
Autoscaling inference well means absorbing spikes without paying for idle GPUs the rest of the time. Here is how to tune it.
AWS Trainium vs NVIDIA GPUs: Custom Silicon for Training Compared
AWS Trainium promises lower training costs than NVIDIA GPUs, but the tradeoff is ecosystem maturity. Here is how the two compare for real workloads.
Set Up a Fault-Tolerant Spot Training Job From Scratch
Build a training job that survives spot interruptions through checkpointing, automatic resume, and a sensible fallback.
Setting Up GPU Cloud Budget Alerts Before Bills Explode
A beginner-friendly guide to GPU cloud budget alerts: thresholds, anomaly detection, and hard stops that catch runaway spend before it hurts.
Continuous Batching: The Trick Behind High-Throughput LLM Serving
Continuous batching keeps the GPU busy by swapping finished requests for new ones mid-flight. It is why modern serving is so efficient.
GPU Cloud Billing Units: Per-Second, Per-Minute, and Per-Hour Compared
Billing granularity quietly shapes your GPU bill. Compare per-second, per-minute, and per-hour pricing and learn which fits your workload.
Cost Per Million Tokens Compared Across Top Inference APIs
How to compare cost per million tokens across inference APIs the right way, accounting for input and output splits, model tiers, and hidden fees.
Set Up GPU Monitoring With Prometheus and Grafana
Build a GPU monitoring dashboard with Prometheus and Grafana so you can spot idle GPUs, thermal throttling, and wasted spend at a glance.
GPU Cloud Marketplaces: How Spot GPU Bidding Actually Works
How GPU cloud marketplaces and spot bidding work: where the cheap capacity comes from, the interruption risk, and how to use it safely.
GPU Sizing for LLM Serving: Matching VRAM to Model Size
Pick a GPU too small and the model will not load; too big and you overpay. Here is how to size VRAM to your model.
Batch Inference: How Async Processing Slashes Token Costs
If your workload can wait minutes or hours, batch inference can cut token costs sharply. Here is when and how to use it.