Intent-driven acquisition

GPU workload guides for the questions buyers actually search

These pages translate live GPU pricing into workload-specific answers: model hosting, fine-tuning, batch inference, and training cost decisions that are easier to rank for than raw SKU pages alone.

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Guide collection

Model-intent and use-case pages

Start with the exact decision you are making, then drill into the relevant GPU, provider, or comparison page once you know which corner of the market matters.

Model intent Cheapest GPU for GLM-4.7-Flash Map the 3B-active GLM-4.7-Flash onto the current 32GB and 48GB GPU market. Model intent Best GPU for Kimi Linear 48B hosting See which 48GB-class GPU is the cheapest practical home for Kimi's long-context 3B-active model. Model intent Best GPU for Qwen3.6 35B-A3B hosting Compare the live cost of a current 48GB coding, vision, and agentic model on tracked providers. Use-case intent Best cloud GPU for fine-tuning Balance 80GB+ memory headroom against hourly spend for adapter and full fine-tunes. Use-case intent Cheapest provider for batch inference Rank providers by their cheapest 24GB+ batch-friendly inference option. Use-case intent H100 vs A100 for training cost Compare H100-family and A100-family rental costs before choosing a training budget.
Agent-assisted setup

Hand this runbook to Claude Code or Codex

Open a terminal in the repository where you want the deployment files, start claude or codex, then paste this prompt. It asks the agent to verify sources and stop before it creates billable infrastructure.

Claude Code quickstart ↗ Codex docs ↗
Deployment prompt 
Use the infrastructure or model context on this page to create a reproducible open-model deployment. Use this guide as the starting context: https://www.getflops.ai/guides. Read the linked model card and provider documentation before choosing hardware or runtime settings. Open every linked primary source and flag any mismatch instead of guessing. Create a deployment folder containing README.md, .env.example with no secrets, a pinned start script or infrastructure manifest, and smoke-test.sh. Make the endpoint OpenAI-compatible where the runtime supports it. Run local/static validation, estimate the billable resources, and stop before provisioning paid infrastructure until I approve.
Guardrails included No secrets in files · verify primary docs · approval before spend