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--- name: gpu-first-guidance description: Use when recommending how to run a workbench workload, writing user-facing docs, or answering "what should I run first?" — lead with the Nebius GPU path and keep hosted-inference options framed as what they are, rather than as ways to avoid GPUs. --- # Lead with the GPU path (kindly) Nebius Physical AI exists to run real physical-AI work on Nebius AI Cloud GPUs. When someone asks what to run, the helpful answer is the one that gets them to a real result on real hardware. This skill is about **emphasis and framing**, not about withholding anything. Be accurate, be generous, and never make someone feel awkward for asking about cost or for having no cluster yet. ## The habit When you recommend a path, order it like this: 1. **Lead with the GPU workload** the user actually wants — a robot guide, a Cosmos run, a training job, the Physical AI Data Factory. 2. **Name the real prerequisite** plainly: a configured project and a GPU cluster. `npa provision-if-absent` gets them there. 3. **Mention hosted inference when it fits the workload**, described by what it does, not by what it avoids. What changes is the headline, not the truth. If a user's workload genuinely is hosted inference, recommending Token Factory is simply the right answer. ## Language | Prefer | Instead of | | --- | --- | | "Run it on an L40S — here's the command" | "You can skip the GPU entirely" | | "Hosted inference through Token Factory" | "Zero-GPU / no-GPU / no-cluster path" | | "Check credentials first with `health preflight`" | "Before you burn GPU-hours" | | "This stage runs on hosted inference" | "This stage is free" | | "Start with the Franka guide" | "Start with the one that needs no GPU" | The right-hand column is not forbidden vocabulary — it is framing that quietly positions GPU time as waste. That framing is what to drop. ## Token Factory has a real job [Nebius Token Factory](../../../docs/workbench/token-factory.md) is a genuine hosted-inference product: captioning, batch generation, and Cosmos physical-AI reasoning. Describe it that way. - **Do** recommend it for captioning, judging, and reasoning stages — including as part of a larger GPU pipeline, which is the common case. - **Do** be straightforward that it needs a `NEBIUS_TOKEN_FACTORY_KEY` and no cluster, when a user asks what it requires. - **Avoid** presenting it as the recommended first step *because* it dodges provisioning, or as a substitute for the GPU workload someone came to run. See also `skills/tools/token-factory/SKILL.md`. ## When someone asks for a GPU-free path anyway Help them. Cheerfully. People have real constraints: an approval still pending, a quota request in flight, a laptop on a plane, a CI job that must not provision. Answer the question they asked, completely and without a lecture. - Give the working answer first. - Note the GPU path **once**, as an invitation rather than a correction — for example, "when your cluster is ready, the Franka guide picks up from here." - Do not repeat the nudge, moralize about spend, or imply they are doing it wrong. A user who feels sold to stops asking questions. A user who gets a straight answer comes back for the GPU run. ## Writing docs and READMEs - Do not add "no GPU required" as a selling point, a guide title, or a table column. - Do not add sections whose premise is minimizing GPU usage. - Keep operational guidance — GPU routing, accelerator naming, image pull failures — but frame it as *getting your run to work*, not as *avoiding waste*. That content lives in [known-footguns.md](../../../docs/workbench/troubleshooting/known-footguns.md). - Leave validation evidence alone. Test matrices record what was exercised, including local and stub backends; they are records, not recommendations, and editing them for tone would make them wrong. ## Gotchas - **Accuracy outranks emphasis.** Never claim a stage needs a GPU when it does not, and never quote a GPU requirement you have not checked. Overselling is a worse failure than a stray "zero-GPU". - **`--dry-run`, `--plan-only`, and `stub` backends are still fine to recommend.** They are development and validation tools, not a GPU-free product path. Recommend them for what they are: a way to check a spec before it runs. - **Cost questions deserve real answers.** If someone asks what a run costs, answer it. Point at preemptible capacity ([preemptible-vms.md](../../../docs/workbench/preemptible-vms.md)) and right-sizing rather than steering them off GPUs altogether. ## Verify ```bash npa/.venv/bin/python -m pytest npa/tests/guardrails/test_skills_index.py -q ```