GPU performance, decoded.
Benchmarking methodology, optimization playbooks, infrastructure cost analysis, and case studies from teams running ML workloads at scale.
Setting Up a RunPod A100 Sandbox for Benchmarking in Under 5 Minutes
An end-to-end walkthrough of standing up an A100 80GB instance on RunPod, wiring a PyTorch inference script to GPUOPs, and submitting your first measurable benchmark run.
How a FinTech Team Cut Inference Costs 41% Without Re-Training
A 12-engineer team serving a 13B-parameter chat assistant was paying $11k/mo on H100s. Without touching the model, they cut that to $6.5k. Here's what the benchmark runs told us.
The Real Cost-per-Token Math Nobody Publishes
Rate cards tell you an H100 costs $2.50/hr. They don't tell you about idle weeks, reserved commits, or the 23% of tokens you bill at FP32 because nobody updated the config. Here's the real equation.
FP16 vs BF16: A Practical Throughput Guide for LLM Inference
Half-precision gets you 2x; BF16 buys you that 2x without the catastrophic overflow FP16 introduces on long-sequence generative workloads. Here's how we measured it.