Blog Topics
Every post on the Spheron blog belongs to one of 16 topics, grouped below by the question you're actually trying to answer. Pick the one that matches what you're building, from choosing a GPU to running a compliance review on a self-hosted deployment, and start with the pillar guide.
Choosing & Comparing
GPU Selection
How to choose the right GPU for training, fine-tuning, or inference. Compares VRAM, compute, and cost across GPU models so you rent the right one for the job.
Browse all →GPU Deep Dives
In-depth breakdowns of individual GPU models: memory bandwidth, architecture, compute specs, and real-world performance for training and inference workloads.
Browse all →GPU Pricing
Hourly GPU rental rates, cost-per-token breakdowns, and pricing surveys across cloud providers, so you know what training and inference should actually cost.
Browse all →Serving & Inference
vLLM
Practical vLLM guides covering production deployment, throughput tuning, continuous batching, and benchmarks against other serving stacks on rented GPUs.
Browse all →Serving Stacks
Guides and benchmarks for SGLang, TensorRT-LLM, TGI, and other LLM serving stacks, comparing throughput and latency so you pick the right one for production.
Browse all →Inference Optimization
Practical techniques for faster, cheaper inference: KV cache management, continuous batching, quantization, FP8, and prefill-decode disaggregation on GPUs.
Browse all →MoE Inference
How to serve mixture-of-experts models in production: expert parallelism, routing, memory layout, and throughput tuning on multi-GPU rented clusters today.
Browse all →Training & Infrastructure
LLM Training
Guides on pretraining, fine-tuning, LoRA, and RLHF for large language models, with practical setup steps for running training jobs on rented GPU clusters.
Browse all →Infrastructure
Networking, orchestration, storage, and multi-node cluster design for AI workloads, covering the infrastructure decisions that determine training performance.
Browse all →Kernel Development
Writing and optimizing CUDA and Triton kernels for GPU workloads, covering memory access patterns, occupancy, and profiling techniques for faster training.
Browse all →Agents, Use Cases & Compliance
AI Agents
Running agentic workloads in production: infrastructure choices, serving patterns, and GPU sizing for AI agents that plan, call tools, and run continuously.
Browse all →Industry Use Cases
Industry-specific guides for self-hosting AI models in healthcare, finance, legal, and other regulated verticals, on infrastructure you control end to end.
Browse all →Data Privacy
Self-hosting AI models for data privacy, regulatory compliance, and data sovereignty, so sensitive workloads never leave infrastructure that you control.
Try It on Real GPUs
The GPUs behind these guides are the ones you can rent here: H100s, H200s, B200s, and more, billed per minute with no contracts and no minimum. Pick one and you are live in under two minutes.