Blog Topics

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.

In-depth breakdowns of individual GPU models: memory bandwidth, architecture, compute specs, and real-world performance for training and inference workloads.

Hourly GPU rental rates, cost-per-token breakdowns, and pricing surveys across cloud providers, so you know what training and inference should actually cost.

Head-to-head comparisons of Spheron against other GPU cloud providers, covering pricing, availability, and setup time to help you pick the right platform.

Practical vLLM guides covering production deployment, throughput tuning, continuous batching, and benchmarks against other serving stacks on rented GPUs.

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.

Practical techniques for faster, cheaper inference: KV cache management, continuous batching, quantization, FP8, and prefill-decode disaggregation on GPUs.

How to serve mixture-of-experts models in production: expert parallelism, routing, memory layout, and throughput tuning on multi-GPU rented clusters today.

Step-by-step tutorials for deploying open-source models on rented GPU cloud infrastructure, from environment setup to serving your first inference request.

Guides on pretraining, fine-tuning, LoRA, and RLHF for large language models, with practical setup steps for running training jobs on rented GPU clusters.

Networking, orchestration, storage, and multi-node cluster design for AI workloads, covering the infrastructure decisions that determine training performance.

Writing and optimizing CUDA and Triton kernels for GPU workloads, covering memory access patterns, occupancy, and profiling techniques for faster training.

How to cut GPU compute costs with spot instances, reserved commitments, utilization tracking, and budget controls, without slowing down training or inference.

Running agentic workloads in production: infrastructure choices, serving patterns, and GPU sizing for AI agents that plan, call tools, and run continuously.

Industry-specific guides for self-hosting AI models in healthcare, finance, legal, and other regulated verticals, on infrastructure you control end to end.

Self-hosting AI models for data privacy, regulatory compliance, and data sovereignty, so sensitive workloads never leave infrastructure that you control.